Performance Analysis of Machine Learning Techniques for Multi-Organ Cancer Detection and Classification: A Comparative Study
M L V A Priya, M. Venkata Subbarao · 2023
Cancer is a leading cause of mortality worldwide, and early detection is crucial for successful treatment and improved patient outcomes. In recent years, Machine Learning (ML) techniques have shown promising potential in assisting medical professionals with accurate cancer detection and classification. This research paper presents a comprehensive investigation and comparative analysis of various ML algorithms for multi-organ cancer detection and classification. The dataset includes wide variety of images taken from different part of organs for clinical inspections. The dataset is further divided into different cancers groups and the images are further processed using preprocessing techniques and then features are extracted from the processed images. These features are taken as the inputs for the ML algorithms for identification of cancers tumours. This paper presents investigations on different organ cancers using Support Vector Machines (SVM), K-nearest Neighbors (KNN), Decision Trees (DT), and Ensemble classifiers (EC). Different performance metrics are measured to know the ability of each classifier in cancer detection. Results depicted that SVM and Ensemble classifiers are performed better than the other classifiers.