Deep learning model for fully automated breast cancer detection system from thermograms

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

Overview of Cancer Control in Armenia and Policy Implications

Cancer is the second leading cause of death in Armenia. Over the past two decades, the country has seen a significant rise in cancer morbidity and mortality. This review aims to provide up-to-date info about the state of cancer control in Armenia and identify priority areas of research. The paper analyzes published literature and local and international statistical reports on Armenia and similar countries to put numbers into context. While cancer detection, diagnosis, and treatment are improving, the prevalence of risk factors is still quite high and smoking is widespread. Early detection rates are low and several important screening programs are absent. Diagnosis and treatment methods are not standardized; there is a lack of treatment accessibility due to insufficient government coverage and limited availability of essential medicines. Overall, there is room for improvement in this sector, as research is limited and multidisciplinary approaches to the topic are rare.

DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.