Prostate Cancer Detection using Deep Learning Models on Histopathological Image Slides: An Experimental Analysis

Accurate analysis of tissue samples is important for the detection and treatment of prostate cancer, a disease prevalent in males worldwide. The Gleason score is an important measure system that is used by doctors to grade prostate cancer. It shows how likely the cancer will spread based on the distribution of the cancer cells appear under a microscope. Manual assessment of cells from digitized stained biopsy images is pathologist-dependent, subjective, and tends to produce variable and sometimes conflicting evaluations. Recent deep learning advances have given promising outcomes to assess benign and malignant cells which leads to accurate and efficient grading, overcoming weaknesses in conventional approaches. In this work, a variety of deep neural networks, such as InceptionV3, ResNet50, and InceptionResNetV2, have been used for prostate cancer diagnosis using histopathology slides. Further, the Gleason score is calculated to predict the severity of prostate cancer. From the experiments, it is observed that InceptionResNetV2 outperformed other models with an accuracy of 91.8%, and closely reflects expert pathologist grading. By blending artificial intelligence with medical practice, these advances pave the way for enhanced accuracy in cancer diagnostics, patient care, and timely detection.