Muhammed Talo

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.

Myocardial infarction (MI), commonly known as heart attack, causes irreversible damage to heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to avoid death. Blood tests and electrocardiogram (ECG) signals are used to diagnose acute MI. However, for an increase in blood enzyme values, a certain time must pass after the attack. This time lag may delay MI diagnosis. Hence, ECG diagnosis is still very important. Manual ECG interpretation requires expertise and is prone to inter-observer variability. Therefore, computer aided diagnosis may be useful in automatic detection of MI on ECG. In this study, a deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI is proposed. For this purpose, the most commonly used technique, convolutional neural network (CNN) is used. Our trained CNN model with the proposed architecture yielded impressive accuracy and sensitivity performance over 99.00% for MI diagnosis on all ECG lead signals. Thus, the proposed model has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units.

Advances in artificial intelligence technologies have made it possible to obtain more accurate and reliable results using digital images. Due to the advances in digital histopathological images obtained using whole slide image (WSI) scanners, automated analysis of digital images by computer support systems has become interesting. In particular, deep learning architectures, are one of the preferred approaches in the analysis of digital histopathology images. The deeper networks trained on large amounts of image data are adapted for different tasks using transfer learning technique. In this study, automated detection of invasive ductal carcinoma (IDC), which is the most common subtype of breast cancers, is proposed using deep transfer learning technique. We have used deep learning pre-trained models, ResNet-50 and DenseNet-161 for the IDC detection task. The public histopathology dataset containing 277,524 image patches were used in our experimental studies. As a result of training on the last layers of pre-trained deep networks, DenseNet-161 model has yielded F-sore of 92.38% and balanced accuracy value of 91.57%. Similarly, we have obtained F-score of 94.11% and balanced accuracy value of 90.96% using ResNet-50 architecture. In addition, our developed model is validated using the publicly available BreakHis breast cancer dataset and obtained promising results in classifying magnification independent histopathology images into benign and malignant classes. Our developed system obtained the highest classification performance as compared to the state-of-art techniques and is ready to be tested with more diverse huge databases.

The brain disorders may cause loss of some critical functions such as thinking, speech, and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the conventional methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute changes in the MRI images, especially in the early stages of abnormalities. Proper selection of the features and classifiers to obtain the highest performance is a challenging task. Hence, deep learning models have been widely used for medical image analysis over the past few years. In this study, we have employed the AlexNet, Vgg-16, ResNet-18, ResNet-34, and ResNet-50 pre-trained models to automatically classify MR images in to normal, cerebrovascular, neoplastic, degenerative, and inflammatory diseases classes. We have also compared their classification performance with pre-trained models, which are the state-of-art architectures. We have obtained the best classification accuracy of 95.23% ± 0.6 with the ResNet-50 model among the five pre-trained models. Our model is ready to be tested with huge MRI images of brain abnormalities. The outcome of the model will also help the clinicians to validate their findings after manual reading of the MRI images.

Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided. Approaches used by investigators are examined, and their contributions to the field are detailed. For this purpose, journal papers have been surveyed according to the methods used. In addition, various deep learning models and experimental studies are described and discussed. A five-class ECG dataset containing 100,022 beats was then utilized for further analysis of deep learning techniques. The constructed models were examined with this dataset, and results are presented. This study therefore provides information concerning deep learning approaches used for arrhythmia classification, and suggestions for further research in this area.

There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided diagnostic systems are a critical step in the early diagnosis and treatment of diseases. Once a pathology image is scanned by a microscope and loaded onto a computer, it can be used for automated detection and classification of diseases. In this study, the DenseNet-161 and ResNet-50 pre-trained CNN models have been used to classify digital histopathology patches into the corresponding whole slide images via transfer learning technique. The proposed pre-trained models were tested on grayscale and color histopathology images. The DenseNet-161 pre-trained model achieved a classification accuracy of 97.89% using grayscale images and the ResNet-50 model obtained the accuracy of 98.87% for color images. The proposed pre-trained models outperform state-of-the-art methods in all performance metrics to classify digital pathology patches into 24 categories.

In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.

Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.

Kidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.

<h2>Abstract</h2><h3>Purpose</h3> Cardiovascular disease (CVD) is a leading cause of death globally. Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for the diagnosis of CVD. The automated and robust detection of CVD from ECG signals plays a significant role for early and accurate clinical diagnosis. The purpose of this study is to provide automated detection of coronary artery disease (CAD) from ECG signals using capsule networks (CapsNet). <h3>Methods</h3> Deep learning-based approaches have become increasingly popular in computer aided diagnosis systems. Capsule networks are one of the new promising approaches in the field of deep learning. In this study, we used 1D version of CapsNet for the automated detection of coronary artery disease (CAD) on two second (95,300) and five second-long (38,120) ECG segments. These segments are obtained from 40 normal and 7 CAD subjects. In the experimental studies, 5-fold cross validation technique is employed to evaluate performance of the model. <h3>Results</h3> The proposed model, which is named as 1D-CADCapsNet, yielded a promising 5-fold diagnosis accuracy of 99.44% and 98.62% for two- and five-second ECG signal groups, respectively. We have obtained the highest performance results using 2 s ECG segment than the state-of-art studies reported in the literature. <h3>Conclusions</h3> 1D-CADCapsNet model automatically learns the pertinent representations from raw ECG data without using any hand-crafted technique and can be used as a fast and accurate diagnostic tool to help cardiologists.

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.

Summary Deep learning has emerged as an effective solution to various text mining problems such as document classification and clustering, document summarization, web mining, and sentiment analysis. In this paper, we describe our work on investigating several deep learning models for a binary sentiment classification problem. We used movie reviews in Turkish from the website www.beyazperde.com to train and test the deep learning models. We also report a detailed comparison of the models in terms of accuracy and time performances. Two major deep learning architectures used in this study are Convolutional Neural Networks and Long Short‐Term Memory. We built several variants of these models by changing the number of layers, tuning the hyper‐parameters, and combining models. Additionally, word embeddings were created by applying the word2vec algorithm with a skip‐gram model on a large dataset (∼ 13 M words) composed of movie reviews. We investigate the effect of using the pre‐word embeddings with these models. Experimental results have shown that the use of word embeddings with deep neural networks effectively yields performance improvements in terms of run time and accuracy.

Arrhythmia is a condition characterized by perturbation of the regular rhythm of the heart. The development of computerized self-diagnostic systems for the detection of these arrhythmias is very popular, thanks to the machine learning models included in these systems, which eliminate the need for visual inspection of long electrocardiogram (ECG) recordings. In order to design a reliable, generalizable and highly accurate model, large number of subjects and arrhythmia classes are included in the training and testing phases of the model. In this study, an ECG dataset containing more than 10,000 subject records was used to train and diagnose arrhythmia. A deep neural network (DNN) model was used on the data set during the extraction of the features of the ECG inputs. Feature maps obtained from hierarchically placed layers in DNN were fed to various shallow classifiers. Principal component analysis (PCA) technique was used to reduce the high dimensions of feature maps. In addition to the morphological features obtained with DNN, various ECG features obtained from lead-II for rhythmic information are fused to increase the performance. Using the ECG features, an accuracy of 90.30% has been achieved. Using only deep features, this accuracy was increased to 97.26%. However, the accuracy was increased to 98.00% by fusing both deep and ECG-based features. Another important research subject of the study is the examination of the features obtained from DNN network both on a layer basis and at each training step. The findings show that the more abstract features obtained from the last layers of the DNN network provide high performance in shallow classifiers, and weight updates of DNN network also increases the performance of these classifiers. Hence, the study presents important findings on the fusion of deep features and shallow classifiers to improve the performance of the proposed system.

Prostate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy.

Abstract The automated segmentation of dental restorations is a critical step in diagnosing dental problems and suggesting the best treatment. Some restorations may be missed during a dental examination, depending on the number of patients, the dentist's experience, and fatigue. Automatic detection of dental restorations based on deep learning has the potential to provide a quick radiological assessment based on the patient's treatment history and pre‐diagnosis. This study presents a deep learning‐based method for automatic detection and classification of amalgam and composite fillings on panoramic images. A total of 250 anonymized panoramic images with amalgam and composite fillings with a resolution of 2048 × 1024 px were used. In this study, U‐Net models with various backbones were employed. The ResNext50 model has achieved the highest pixel accuracy and intersection over union (IoU) performance based on the evaluation of various ResNet and ResNext backbones. The mean IoU value obtained by the model on the test images is 0.767 while the Pixel Accuracy of 99.81% was achieved. Our proposed method demonstrated superior performance compared to similarly conducted studies in the literature. The proposed method can potentially be employed in clinical settings to detect dental restorations automatically. The classification and detection of dental restorations with this model can aid dentistry education at higher institutions as an education tool and make the reporting easier for the dentist.

Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories. The transfer learning technique was employed to accelerate the training process of the network and improve the classification performance of the network. The proposed method achieved an average classification accuracy of 99.2%. The experimental results demonstrate that the proposed technique surpasses state-of-the-art methods in the literature and can be used for any type of bacteria classification tasks.

Segmentation of myocardial infarction (MI) is a crucial task in the field of heart disease theranostics. Cardiac magnetic resonance imaging (MRI) is a well-known non-invasive imaging technique that provides comprehensive insights into the structure and function of the heart. However, manually interpreting myocardial infarction from multiple MRI frames is time-consuming, labor-intensive, and prone to errors. This study aims to develop an end-to-end deep learning framework that can automatically segment myocardial infarction (MI) and persistent microvascular obstruction (MVO) among the normal tissues of left ventricle (LV), normal myocardium (Myo), and the remaining normal foreground (BG). The proposed framework includes various stages, such as cardiac MR image collection, preprocessing via three enhancement techniques, splitting and training set augmentation, selection of the most suitable artificial intelligence (AI) segmentation model, and performance evaluation and comparison. For the multi-class segmentation process, we adopt and develope four AI state-of-the-art models: U-Net, U-Net_VGG16, SegNet, and ResUnet, which are well-regarded for their effectiveness in image segmentation across different computer vision domains. The publicly available benchmark EMIDEC MRI dataset is utilized for training and evaluating the proposed segmentation framework. The ResU-Net achieved the top performance compared to other AI models, recording an overall accuracy (Acc) of 88.48%, recall (Re) of 85.24%, precision (Pre) of 85.46%, F1-score of 85.35, and MIoU of 84.23%. Comparing with the original dataset (without preprocessing), the CLAHE preprocessing improves the ResU-Net segmentation performance by 2.19% and 3.08% in terms of average F1-score and MIoU for all classes (LV, Myo, MI, and MVO). Therefore, the proposed AI segmentation framework demonstrates its potential for effectively performing multi-class segmentation of cardiac diseases from MRI images.

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The “gold standard” for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning.

We consider a communication channel in which the only available mode of communication is transmitting beeps. A beep transmitted by a station attached to the channel reaches all the other stations instantaneously. Stations are anonymous, in that they do not have any individual identifiers. The algor ithmic goal is to assign names to the stations in such a manner that the names make a contiguous segment of positive integers starting from 1. We develop a Las Vegas naming algorithm, for the case when the number of stations n is known, and a Monte Carlo algorithm, for the case when the number of stations n is not known. The given randomized algorithms are provably optimal with respect to the expected time 𝒪(n log n), the expected number of used random bits 𝒪(n log n), and the probability of error.

Pneumonia continues to be the leading cause of child mortality in children under the age of five, and 2,400 children, most of whom are babies under 2 years of age, die from pneumonia. In this study, an automated detection system is proposed for the diagnosis of pneumonia with chest radiography images. With the transfer learning technique, ResNet-152 convolutional neural network was customized to recognize pneumonia from radiography images. With this customized architecture, a recognition success of 97.4% was obtained in the detection of pneumonia disease without any preprocessing of raw data or manual feature extraction on radiography images. This model, which was proposed for the detection of pneumonia, found to be more successful when compared with the other successful studies in the literature.

Bilgisayar destekli cihazların ve sistemlerin sağlık alanında kullanımı oldukça yaygınlaşmıştır. Bu cihaz ve sistemlerin hastalıkların daha hızlı ve erken teşhisine katkısı yüksekti. Özellikle Manyetik Rezonans Görüntüleme (MRI), Bilgisayarlı Tomografi (BT) gibi görüntüleme cihazları; erken teşhisin önemli olduğu hastalıklar özelinde oldukça büyük bir rol oynamaktadır. Nörolojik hastalıklarda da MR ve BT görüntülerinin derin öğrenme modellerinde girdi görüntüsü olarak kullanımı giderek yaygınlaşmaktadır. Bu çalışmada Kaggle sitesi üzerinden elde edilen Alzheimer ve Parkinson hastalıkları teşhisi için “Alzheimer Parkinson 3 Class Data Set” veri setindeki MRI görüntüleri kullanılmıştır. Bu veri seti içerisinde 2561 Alzheimer, 906 Parkinson ve 3010 adet Kontrol (Normal) olmak üzere üç sınıf bulunmaktadır. Bu çalışmada; Alzheimer, Parkinson ve Normal sınıfları, ResNet-18, VGG-16 ve ConvNext mimarisi ile eğitildiğinde sırasıyla %96,2, %95,4 ve %98,9 doğruluk oranı elde edilmiştir. Bunun yanında; Alzheimer ve Parkinson hastalıkları normal sınıfı üzerinde ikili sınıflandırıcılar ile test edilmiştir. Alzheimer- Normal ve Parkinson – Normal sınıfları için eğitilen modellerden ResNet-18 mimarisi sırası ile %82,0 ve %96,1, VGG-16 mimarisi sırası ile %95,4 ve %89,4, ConvNext mimarisi ise %99,4 ve %99,5 başarı oranlarına ulaşılmıştır.

Melanoma, recognized as the most lethal form of cancer, originates from the uncontrolled proliferation of melanocytes, cells responsible for melanin production. Identifying melanoma lesions poses a formidable challenge due to their visual resemblance to non-cancerous lesions. This paper presents an end-to-end skin lesion prediction framework that includes data collection, preprocessing, data augmentation, the state-of-the-art YOLOv8 prediction model, and evaluation procedure. To train and assess the proposed framework, two benchmark medical datasets were collected and used: (1) PH2 and (2) ISIC2018. The preprocessing procedure is conducted to improve prediction performance, and the augmentation process is used to increase the training set size to meet the requirements of the AI model. Subsequently, the state-of-the-art deep learning YOLOv8 model is adopted and fine-tuned as the core prediction component for the proposed framework. The detection results achieved an overall mAPs@0.5 of 99.50% and 97.30% using the PH2 and ISIC2018 datasets, respectively. These promising and encouraging evaluation results appear to be practically useful for establishing an end-to-end prediction framework for skin lesions.

Analysis of pap smear images under a microscope by experts is a laborious and time consuming task. Computer-assisted diagnostic (CAD) systems can simplify this tedious process and allow experts to focus on more critical cases. Effective screening and early diagnosis can help to detect precancerous cells and allow early treatment. In this study, we have used a deep learning approach for classification of cervical cell images which obtained from pap smear slides. The proposed method automatically classifies cervical cell images into five categories without using any pre-processing on raw input images. We have obtained the promising results as compared to the previous studies in the literature. The proposed model can give a second opinion to clinicians in their daily routines and help them to focus on more complex cases.

Meme kanseri, dünya çapında kadınlar arasında en fazla ölümün görüldüğü kanser türüdür. Meme kanseri imgelerinin bilgisayar destekli sistemler yardımıyla hızlı ve doğru bir şekilde sınıflandırılması hayati önem arz etmektedir. Bu çalışmada, meme kanseri imgelerini iyi ve kötü huylu olarak sınıflandırmak için ResNet-50 mimarisi önerilmiştir. Evrişimsel Sinir Ağı tabanlı ResNet-50 mimarisi kullanılarak, açık kaynak BreakHis veri setindeki, meme kanseri imgelerinin ikili sınıflandırılması gerçekleştirilmiştir. ResNet-50 mimarisinin eğitiminde transfer öğrenme yöntemi uygulanmıştır. Önerilen modelin sınıflandırma başarısının, literatürdeki mevcut çalışmalara kıyasla daha yüksek olduğu gözlemlenmiştir. Ayrıca önerilen model, meme kanseri imgeleri üzerinde herhangi bir ön işleme yapmadan verileri otomatik olarak sınıflandırmaktadır.

Face analysis is the process of extracting useful information such as gender, age and race from a face image. In this study, we look at ways of closing the gap between the capabilities of automatic facial recognition and race classification methods. For this purpose, we take the following two steps. (1) We offer a public race dataset consisting of labeled images to overcome the challenges of real-world race estimation tasks. (2) We present a benchmark study to find race of a human face using a pre-trained Convolutional Neural Network (CNN) model on the large-scale face dataset. We are able to achieve a remarkable accuracy of 97.6% in race classification task.

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (Vi <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> ). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile Vi <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs.

Clinical screening of myocardial infarction is important for preventative treatment and risk stratification in cardiology practice, however current detection by electrocardiogram Q-wave analysis provides only modest accuracy for assessing prior cardiac events. We set out to evaluate the ability of a deep neural network trained on the electrocardiogram to identify patients with clinical history of myocardial infarction. We assessed 608 patients at two academic centers with adjudicated history of myocardial infarction. Surface electrocardiograms were used to train a neural network-based model that classifies patients with and without a history of infarction. Endpoints were assessed by clinical record review and accuracy of the model was compared against the manual assessment of pathologic Q waves. The neural network outperformed the accuracy of pathologic Q waves (62%). In training, the model accuracy converged to >98%. Validation was performed by cross-validation (k=5) with validation accuracy 71 ± 5%. Receiver-operator characteristics analysis resulted in a c-statistic of 0.730. Deep learning of a 12-lead ECG can identify features of prior myocardial injury more accurately than clinical Q-wave analysis and may serve as a valuable clinical screening tool.

We consider synchronous distributed systems in which anonymous processors communicate by shared read-write variables. The goal is to have all the processors assign unique names to themselves. We consider the instances of this problem determined by whether the number $n$ is known or not, and whether concurrently attempting to write distinct values into the same memory cell is allowed or not, and whether the number of shared variables is a constant independent of $n$ or it is unbounded. For known $n$, we give Las Vegas algorithms that operate in the optimum expected time, as determined by the amount of available shared memory, and use the optimum $O(n\log n)$ expected number of random bits. For unknown $n$, we give Monte Carlo algorithms that produce correct output upon termination with probabilities that are $1-n^{-Ω(1)}$, which is best possible when terminating almost surely and using $O(n\log n)$ random bits.

Akciğer kanseri, ülkemizde ve dünyada yaygın bir şekilde görülen kanser tipidir ve kansere bağlı ölümlerde ilk sırada yer almaktadır. Akciğer kanserinin erken teşhisi, hastalık seyri hakkında daha bilinçli ilerlemeyi sağlar ve hastanın sağ kalım durumu açısından hayati bir önem taşımaktadır. Son zamanlarda teknolojinin gelişmesiyle birlikte yapay zekâ ve derin öğrenme tabanlı sistemler; Bilgisayarlı Tomografi (BT), Manyetik Rezonans (MR) vb. tıbbi görüntüleme sistemlerinden elde edilmiş verileri kullanarak hastalık teşhisinde hekimlere önemli destek sağlamaktadır. Bu çalışmada akciğer kanserinin BT görüntüleri kullanarak yeni bir Evrişimli Sinir Ağı (ESA) modeli önerilmiştir. Önerilen ESA modelinin sınıflandırma sonuçları, literatürde bulunan diğer ön eğitimli derin öğrenme modellerine göre daha başarılı olduğu için tercih ettiğimiz ResNeXt derin öğrenme modelinin sonuçları ile karşılaştırılmıştır. Modellerin eğitimi ve test aşamaları için açık erişimli akciğer BT görüntülerinin bulunduğu bir veri seti kullanılmıştır. Çalışma sonucunda, önerilen ESA modelinin %99 doğruluk oranı ile ResNeXt mimarisine göre daha yüksek performans sergilediği gözlemlenmiştir. Ayrıca mevcut çalışmadaki görüntülerde herhangi bir özellik çıkarımı yöntemi kullanılmadan görüntüler ham hali ile sınıflandırılmıştır. Ve önerilen ESA modelinin, literatürde yapılan benzer çalışmalarda kullanılan yöntemlere göre daha az katman sayısının olmasının yanında sınıflandırma başarısının da daha yüksek olduğu gözlemlenmiştir.

We consider synchronous distributed systems in which processors communicate by shared read- write variables. Processors are anonymous and do not know their number n. The goal is to assign individual names by all the processors to themselves. We develop algorithms that accomplish this for each of the four cases determined by the following independent properties of the model: concurrently attempting to write distinct values into the same shared memory register either is allowed or not, and the number of shared variables either is a constant or it is unbounded. For each such a case, we give a Monte Carlo algorithm that runs in the optimum expected time and uses the expected number of O(n log n) random bits. All our algorithms produce correct output upon termination with probabilities that are 1−n^{−Ω(1)}, which is best possible when terminating almost surely and using O(n log n) random bits.

Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories. The transfer learning technique was employed to accelerate the training process of the network and improve the classification performance of the network. The proposed method achieved an average classification accuracy of 99.2%. The experimental results demonstrate that the proposed technique surpasses state-of-the-art methods in the literature and can be used for any type of bacteria classification tasks.

Atriyal Fibrilasyon (AFib), yaşlılarda ve hatta herhangi bir kalp hastalığı olmayan gençlerde bile görülebilen yaygın bir kalp ritim bozukluğudur. AFib; inme, kalp yetmezliği ve ani ölümlere neden olabilir. Tüm bu mevcut ve gelecekteki endişeler, dünya çapında AFib'in erken tespitinde önemli önlemlerin alınmasını gerektirir. Elektrokardiyografik (EKG) dalga formları, AFib gibi anormal kalp ritimlerini saptamak için en güvenilir yöntem olarak kabul edilmektedir. Ancak EKG sinyallerinin karmaşıklığı ve doğrusal olmaması nedeniyle bu sinyalleri manuel olarak analiz etmek zordur. Bunun yanı sıra, EKG sinyallerinin yorumlanması kişiye özgü ve uzmanlar arasında farklılık gösterebilmektedir. Bu nedenle otomatik ve güvenilir bir AFib algılama için bilgisayar destekli teşhis (BDT) sistemlerinin kullanımı önemlidir. BDT sistemleri, EKG sinyallerinin değerlendirilmesinin objektif ve doğru olmasını sağlayacak potansiyele sahiptir. Bu çalışmada, derin öğrenme yapısı kullanılarak EKG sinyallerinden otomatik AFib tespiti gerçekleştirilmiştir. Derin öğrenme algoritmalarından evrişimli sinir ağı (ESA) mimarisinin AFib sınıflandırma probleminde kullanımı için çalışma kapsamında derin bir ağ modeli tasarlanmıştır. Kullanılan verisetinde normal sinüs ritimlerinin (SR) yanısıra AFib ve Atriyal Flutter (AFL) aritmileri bulunmaktadır. AFib ve AFL sınıfları birleştirilerek model çıkışında SR ve AFib ayırımının otomatik yapılması sağlanmıştır. Önerilen model, 2222 SR ve 2218 AFib tanısı alan kişilere ait her biri 5000 örneğe sahip EKG sinyali içeren veri seti üzerinde uygulanmıştır. Çalışma kapsamında hazırlanan ESA modeli, test aşamasında sırasıyla %95.09 hassasiyet, %97.27 özgüllük ve %97.26 kesinlik değerlerine ulaşmıştır. Modelin test verileri üzerindeki doğruluk oranı %96.17 olarak elde edilmiştir.