Deep Learning Applications in Histopathological Images

Breast cancer is a neoplasm that mainly affects women above the age of 45. However, an increase in the incidence of this disease among young women has been observed. Although it is considered a cancer with a good prognosis when diagnosed early, early detection remains a challenge. In Brazil, the mortality rate due to breast cancer remains high, which is directly related to the late diagnosis of the disease. To contribute to the reduction of this rate, the development of effective early detection techniques is essential. These techniques can assist in diagnosing the disease at its initial stages, enabling quicker treatment and thereby increasing the chances of a cure. Computer-aided detection and diagnosis systems have been developed and improved in the field of computing. These systems base their accuracy and reasoning on data obtained through a combination of computer vision techniques, such as pattern recognition and machine learning. When applied, these techniques assist doctors and specialists in data analysis to provide diagnostic support and treatment planning. This significantly enhances a patient’s chances of recovery. More recently, within the machine learning field, Deep Learning has become a prevalent focus of research due to its ability to automatically extract relevant features for the target task. In this work, the methodology proposed employs Convolutional Neural Networks for machine learning. While the results obtained are not superior to those in the literature, they are close and generally require fewer computational resources for training the selected networks after the selection process.