Review of machine learning algorithms for breast and lung cancer detection
Krishna Pai, Rakhee Kallimani, Sridhar Iyer, Rahul Jashvantbhai Pandya · 2023
In the innovative field of medicine, malignant growth has attracted significant attention from the research community due to the fact that genuine treatment of such diseases is currently unavailable. In fact, diseases of such types are so severe that the patient's life can be saved only when the disease is identified in the early stage, i.e., stages I and II. To accomplish this early-stage disease identification, machine learning (ML) and data mining systems are immensely useful. Specifically, using the large available data existing over the web-based repositories, ML techniques and data mining can be implemented to gather valuable information in view of cancer identification or classification. This chapter is oriented towards the aforementioned with an aim to conduct a point-by-point study of the most recent research on various ML techniques such as Artificial Neural Networks (ANNs), k-Nearest Neighbours (KNN), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs). The main contribution of the chapter is the review followed by the decision on the 'best' calculation for an a priori finding of breast and lung malignancy. The crude information from the mammogram or tomography images or the datasets which have been obtained are utilized as the information. The pre-processing of the data and related processes are conducted following which the best prediction model is obtained. Also, the processing time for testing, training, and compliance of all the cases is determined. The results of this study will aid in determining the most appropriate ML technique for the detection of tumours in breast and lung cancer.