Enhanced Artificial Neural Networks for Prostate Cancer Detection and Classification

Histopathological scans identify prostate cancer, which medical professionals say is common in men. Pathologists’ modest differences in prostate cancer grading using the Gleason system persist. This work replicates automated prostate cancer segmentation and classification. The proposed method uses gland-oriented segmentation and classification. Prostate cancer is one of the major killers of men in the USA. Complex masses can cause radiologists to overlook prostate cancer. Recent prostate cancer detection technologies are weak. A robust deep learning ANN and transfer learning are used in this research. Decision Tree, SVM with different kernels, and Bayes are compared. GoogleNet models and machine learning classifiers employ cancer MRI database properties. Morphological, entropy-based, textural, SIFT, and elliptic Fourier descriptors are examples. Performance assessment uses numerous indicators. Specificity, sensitivity, and accuracy transfer learning and ANN (Google Net) yielded the greatest results.