Automated Histopathological Analysis for Accurate Grading of Prostate Cancer Using CNNs and SVMs

Histopathological analysis is an essential but difficult technique for grading prostate cancer; traditional systems rely on human assessment or rudimentary machine learning methods, which frequently produce inconsistent and less accurate results. Existing automated techniques, which use on logistic regression or decision trees, have difficulty capturing intricate tissue patterns and exhibit poor cross-dataset generalization. The study provides a hybrid system that pairs Support Vector Machines (SVMs) for classification with Convolutional Neural Networks (CNNs) and a novel attention mechanism (channel-wise and spatial attention) for increased feature extraction. The technique achieves higher accuracy and reliability in prostate cancer grading by using robust preprocessing techniques and ensemble learning with several SVM. The proposed system outperformed existing systems with 88.7% accuracy, outperforming them by 75.5% and 80.4%, respectively. It also provided improved precision, recall, and F1-score, ensuring more consistent and generalizable cancer grading results. The results show a significant improvement over the existing systems.