Use of AI and Machine Learning for the Analysis of Cellular Images in Breast Cancer
Reena Thakur, Pradnya Sulas Borkar, Parul Bhanarkar, Prashant Panse · 2024
Cancer is one of the most dangerous illnesses, but no long-term cure exists. One of the most common cancers is breast cancer. In India, a woman receives a breast cancer diagnosis every four minutes. In India, a woman succumbs to breast cancer once every eight minutes. Early identification of breast cancer boosts treatment options and increases survival prospects. Breast cancer is one of the primary diseases that kill women today. The advanced engineering of natural image classification approaches and artificial intelligence systems has been heavily utilized for the breast cancer image classification issue. Doctors and medical professionals can acquire a second opinion while also saving time by using automatic picture classification. Surprisingly, few review papers thoroughly explain breast cancer image classification techniques, selection methods, feature extraction, classification measuring parameterizations, significant problems, and image classification findings in breast cancer identification despite many publications on breast cancer image classification. The efficient use of artificial intelligence and machine learning in detecting and treating many deadly diseases has increased patient survival rates by facilitating early diagnosis and treatment. Deep learning has been developed to examine the key elements influencing the diagnosis and management of serious diseases. The comprehensive analyses of prior research on the early identification and management of breast cancer using genetic sequencing or histopathological imaging will be discussed. Several semi-supervised and unsupervised methods have been applied to classify breast images, including convolutional neural networks (CNNs), support vector machines (SVMs), and Bayesian methods. These methods include logic-based classifiers like the random forest (RF) algorithm.