NATIONAL CONFERENCE ON NEW TECHNOLOGIES IN ELECTRICAL AND COMPUTER ENGINEERING | Year:2022 | دوره:5 |Papers Count:2

in general, using thermal images and applying image processing on them with the help of deep learning models has facilitated the early diagnosis of breast cancer for doctors and has accelerated the treatment process. Since screening has been a challenging and vital issue for a long time, this study has investigated various imaging methods in general and classified each based on their advantages and disadvantages. However, thermal imaging is particularly discussed in this paper. Thermal imaging makes it possible to identify tumors in the early stages by examining the temperature distribution in both breasts. Due to being a non-invasive screening method and not involving any physical touch, injections or the use of special tools during the process, thermal imaging is considered as more preferred among the medical practitioners. The interpretation of thermal images and its classification into categories such as normal and abnormal for cancer diagnosis is carried out by deep learning models such as convolutional neural network (CNN), U-NET network, etc. This article provides a review of recent studies done in the field of breast cancer diagnosis using deep learning models in thermal images. According to the results reported in recent researches, it seems that the combination of U-NET and CNN models enjoys the best result with 99. 33% accuracy and 100% sensitivity while the weakest performance goes to Bayesian classification with the accuracy of 71. 88% and the sensitivity of 37%.

In recent years, applications of machine learning methods have been widely used to solve complex challenges in various applications, such as medical, financial, environmental, marketing, security and industrial applications. The ability of machine learning to examine large volumes of data and identify relationships between them and provide patterns for interpreting data. Machine learning can help increase the reliability, performance, predictability and accuracy of diagnostic systems for many medical conditions. It is very likely that the next generation of healthcare professionals (medical and nursing) will encounter a set of innovations that work with artificial intelligence and on their own, and may take time during the course of their professional responsibilities. They do not have enough to learn about the machine learning frameworks that drive these systems. Training aspiring physicians and medical care providers with appropriate basic machine learning courses, as part of postgraduate education and medical and nursing education, is likely to train them as high-tech physicians and providers of modern medical services will convert in the future.