CAMELYON 17 Challenge: A Comparison of Traditional Machine Learning (SVM) with the Deep Learning Method

Abstract

The pathologist’s diagnosis is crucial in identifying and categorizing pathological cancer sections, as well as in the physician’s subsequent evaluation of the patient’s condition and therapy. It is recognised as the “gold standard”; however, both objective and subjective pathological diagnoses have limits, such as tissue corruption resulting from the nonstandard collection of diseased tissue, nonstandard tissue fixation or delivery, or a lack of necessary clinical data. In addition, diagnostic pathology encompasses too much information; thus, it requires time and effort to grow a trained pathologist. Consequently, computer-assisted diagnosis has become an essential tool for replacing or assisting pathologists with computer technology and graphical development. In this regard, the CAMELYON 17 competition was designed to identify the best algorithm for detecting cancer metastases in the lymph. Each participant was given 899 whole-slide photos for the development of their algorithms. More than 300 people enrolled on the competition. CAMELYON 17 is primarily focused on the categorization of lymph node metastases. The TNM classification system is the primary classification system. Participants at CAMELYON 17 mostly use categorization and learning techniques in deep learning and machine learning. In order to get a better understanding of the top-selected algorithms, we examine the advantages and limitations of traditional machine learning and deep learning for classifying breast cancer metastases.