Classification of chest X-rays by divergence-based convolutional neural network

Abstract

The importance of imaging methods in the field of health is increasing day by day with the opportunities provided by technology. Imaging without physical intervention is both more convenient and less costly for the patient and one of the ways to diagnose faster. Diagnosis of diseases and taking action, especially in the early stages of epidemic diseases, are among the most effective methods in the fight against these diseases. Correct treatments are applied against diseases that can be differentiated from each other, thus the patient's recovery is ensured. Chest radiographs are also one of the most frequently used methods in the field of imaging, and the damage caused by various viruses or bacteria to the lung can be understood and diagnosed with X-ray images. At the same time, it ensures that health systems continue with minimum damage by providing the necessary data for health workers to take precautions in case of an infectious side of the disease. Covid-19, which entered our lives towards the end of 2019 and caused the death of millions of people in more than 2 years, directly damages the lungs and causes the lungs to not perform their functions. Problems that occur in the lungs by reducing oxygen saturation cause many issues, especially respiratory problems in patients. Some of the problems seen in patients with suspected Covid-19 occur in the lungs and these changes caused by the disease can be detected by X-rays. However, it is not known to what extent the effects of viruses such as Covid-19, which have been in existence for years but have the capacity to infect people in the near future, and it is not possible to deliver treatment techniques to all parts of the world in a short time. In other words, it is a very long and laborious process to be able to effectively diagnose diseases that we can call new diseases that come into our lives by health workers around the world. For this reason, engineering applications in the field of medical imaging are promising in many respects. For example, AI-assisted engineering applications, which have become very common recently, bring many advantages. These achievements can be listed as follows: •Detailed analysis opportunity •Instant access to developments in the world •Ability to be easily updated •Possibility to get results with high accuracy and fast •To alleviate the burden of healthcare workers •Cost-reducing contributions with fewer employees •Reaching areas with low or limited access to the health system •Reducing the contagious risk of the disease by reducing direct or indirect contact with patients •Creating systems whose accuracy and reliability are increasing day by day with continuously trained models. •Opportunity to create models trained with more examples than specialist doctors can see throughout their career Although the developments mentioned are undeniably positive, the physical hardware needs that come with artificial intelligence supported applications should not be ignored.