Since COVID-19 is a worldwide pandemic, COVID-19 detection using a convolutional neural network (CNN) has been an extraordinary research technique. In the reported studies, many models that can predict COVID-19 based on deep learning methods using various medical images have been created; however, clinical decision support systems have been limited. The aim of this study is to develop a successful deep learning model based on X-ray images and a computer-assisted, fast, free and web-based diagnostic tool for accurate detection of COVID-19. Methods: In this study a 15-layer CNN model was used to detect COVID-19 using X-ray images, which outperformed many previously published CNN models in terms of classification. The model performance is evaluated according to Accuracy, Matthews Correlation Coefficient (MCC), F1 Score, Specificity, Sensitivity (Recall), Youden's Index, Precision (Positive Predictive Value: PPV), Negative Predictive Value (NPV), and Confusion Matrix (Classification matrix). In the second phase of the study, the computer-aided diagnostic tool for COVID-19 disease was developed using Python Flask library, JavaScript and Html codes. Results:The model to diagnose COVID-19 has an average accuracy of 98.68 % in the training set and 96.98 % in the testing set. Among the evaluation metrics, the minimum value is 93.4 % for MCC and Youden's index, and the maximum value is 97.8 for sensitivity and NPV. A higher sensitivity value means a lower false negative (FN) value, and a low FN value is an encouraging outcome for COVID-19 cases. This conclusion is crucial because minimizing the overlooked cases of COVID-19 (false negatives) is one of the main goals of this research. Conclusions: In this period when COVID-19 is spreading rapidly around the world, it is thought that the free and web-based COVID-19 X-Ray clinical decision support tool can be a very effective and fast diagnostic tool. The computer-aided system can assist physicians and radiologists in making clinical decisions about the disease, as well as provide support in diagnosis, follow-up, and prognosis. The developed computer-assisted diagnosis tool can be publicly accessed at http://biostatapps.inonu.edu.tr/CSYX/..

Objective: One of the most significant cancers impacting the health of women is breast cancer. This study aimed to provide breast cancer classification (benign and malignant) using the transfer learning method on the ultrasound images. Methods: In the present study, a public imaging dataset was used for the breast cancer classification. Transfer learning technique was implemented for the detection and classification of breast cancer (benign or malignant) based on the ultrasound images. The current research includes data of 150 cases of malignant and 100 normal cases obtained from the Mendeley data. The relevant dataset was partitioned into training (85% of the images) and validation (15% of the images) sets. The present study implemented Teachable Machine (teachablemachine.withgoogle.com) for predicting the benign or malignant of breast cancer tumor based on the ultrasound images. Results: According to the experimental results, accuracy, sensitivity and specificity with 95% confidence intervals were 0.974 (0.923-1.0), 0.957 (0.781-0.999) and 1 (0.782-1.0), respectively. Conclusion:The model proposed in this study gave predictions that could be useful to clinicians in classifying breast cancer based on ultrasound images. Thus, this system can be developed in mobile, web, or alternative environments and offered as a computer-aided system for the use of radiologists, pathologists or other healthcare professionals in hospitals.

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Aim: In this study, by using appropriate video/image processing techniques and CNN architecture, it is aimed to develop user-friendly software for healthcare professionals with various methods such as detection, identification, classification and tracking of polyps contained in the endoscopic images. Material and Methods: The dataset consisted of 345 images in total. These images are images described and validated by medical doctors (experienced endoscopists) of several classes, consisting of hundreds of images for each class, such as anatomical milestones, pathological findings, or gastrointestinal procedures in the digestive tract. The images were obtained from the web address https://datasets.simula.no/kvasir, which is open source for research and educational purposes. CNN and Max-Margin object detection method (MMOD), one of the deep neural network architectures in the Dlib library, was used in the modeling phase. In the evaluation of model performance, precision, recall, F1-score, average precision (AP), mean average precision (mAP), optimal localization recall precision (oLRP), mean optimal LRP, (moLRP) and intersection over union (IoU) were used. Results: In the implementation of the study, when the previously described steps on the open access video image dataset related to the colonic polyps were performed, all performance metrics examined in the training dataset were 100%, while precision of 98%, recall of 94%, F1-score of 94%, AP of 89% and mAP of 89%, oLRP of 48% and moLRP of %48 were calculated on the testing dataset. Conclusion: Considering the values of the calculated performance criteria, it was found that the proposed system gave successful predictions in the diagnosis of gastrointestinal polyps.

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