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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.

China's aging population is a significant threat to its future. Observing the habits and physical conditions of the elderly who live long lives could effectively accumulate experience in preventing the dangers of aging for society. This work studied the association between dietary habits and heart disease in the long-lived elderly. The dataset was collected in the Chinese Longitudinal Healthy Longevity Survey (CLHLS), including 884 heart disease patients and 5592 ordinary people in the age range between 47 to 117. In three grouping approaches, logistic regression analysis investigated the relationship between dietary factors and heart disease (no grouping, age, sex). According to the logistic regression analysis, the frequency of meat and sugar consumption was positively related to the risk of heart disease. On the contract, a negative relationship between the amount of staple food per day and heart disease was observed in all three grouping methods. As a result, this study proposed that the elderly consume more protein and use appropriate animal fat as cooking oil to lower their risk of heart disease.