Author Description

To protect human health and to monitor diseases, medical imaging and interpretation of these images are very important. In recent years, the use of medical imaging devices has increased with the contribution of technological developments. Intensive work for the development of medical imaging technologies is still ongoing [1]. It is estimated that there will be 18.1 million new cancer cases in 2018, and it is estimated that 9.6 people will lose their life [2]. Early detection is crucial to get rid of many dangerous diseases that threaten human life. However, considering the number of specialist doctors compared to the world population, the difficulty of early diagnosis is understood. In low-income countries, the number of experts is quite low. In particular, medical images take time to be examined due to their size and texture. For example, the size of the histopatho-logical images is up to almost 100,000 pixels. In this case, the working hours of the experts increase considerably, while the other patient tissues are waiting for analysis. Fortunately, au-tomatic image analysis is significantly improved with the use of artificial intelligence methods in image processing. Medical image analysis researchers follow developments in the field of classical image processing. Besides, there are many successful methods just for medical image analysis [3,4]. Artificial intelli-gence methods contribute considerably to the development of the image processing area. In particular, by learning the problem, its ability to mimic the actions and decisions of an expert has created a breakthrough in the field of medical image processing. However, some prerequisites must be fulfilled for this method to be successful. Many problems, such as the creation of suitable architecture, the provision of adequate equipment, computational problems, and processing time are not seen as significant prob-lems today.