Machine Learning Approaches for MRI Image Analysis-Based Prostate Cancer Detection
Shivlal Mewada, Pradeep Sharma · Advances in computational intelligence and robotics book series · 2023
The sooner the patient receives a diagnosis for their condition, the higher their chances will be of surviving it. As is the case with conventional diagnosis, medical imaging is analyzed by trained professionals who look for any signs that the body may be displaying cancerous tendencies. The great quality and multidimensionality of MRI images need the use of an appropriate diagnostic system in addition to CAD tools. Because it is useful, researchers are now concentrating their efforts on developing methods to improve the accuracy, specificity, and speed of these systems. A model that is efficient in terms of image processing, feature extraction, and machine learning is presented in this study. This chapter presents machine learning techniques for prostate cancer detection by analyzing MRI images. Image preprocessing is done using histogram equalization. It improves image quality. Image segmentation is performed using the fuzzy C means algorithm. Features are extracted using the gray level co-occurrence matrix algorithm. Classification is performed using the KNN.