Prostate cancer diagnosis utilizes Gleason grading to analyze biopsy images to establish cancer severity levels. The analysis of prostate biopsy images is an important step in automating the Gleason grading system, which helps in prostate cancer diagnosis and prognosis. The subjective evaluation of manual grading methods exposes vulnerabilities since they lead to inconsistent results so automated solutions have become essential for precision and reliability. Present machine learning algorithms show insufficient robustness because they incorporate inadequate feature extraction approaches together with inadequate classifier choices. An ensemble Extra Trees model with characteristics from prostate biopsy images serves as the proposal for Gleason grading classification. The HSV color space produces three statistics (Mean, Standard Deviation, and Skewness) from colors with addition of entropy alongside four texture features derived from GLCM analysis which includes Contrast, Energy, Homogeneity, and Correlation. The proposed model receives evaluation against several classifiers which include Nearest Neighbors, Linear SVM, Decision Tree, and Random Forest. The ensemble Extra Trees classifier reaches 99% accuracy during testing which proves better than baseline models thus indicating its potential in trustworthy prostate cancer grading. The significance of this research is to improve the accuracy and efficiency of Gleason grading in prostate biopsy images using machine learning, aiding in early diagnosis and better treatment planning for prostate cancer.

Prostate Biopsy Gleason Grading Machine Learning Texture Analysis Extra Trees Classifier