Prostate Biopsy Image Gleason Grading Classification using Machine Learning DOI Open Access
Sheshang Degadwala, Divya Midhunchakkaravarthy, Shakir Khan

et al.

Journal of Innovative Image Processing, Journal Year: 2025, Volume and Issue: 7(1), P. 146 - 160

Published: March 1, 2025

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

Language: Английский

Prostate Biopsy Image Gleason Grading Classification using Machine Learning DOI Open Access
Sheshang Degadwala, Divya Midhunchakkaravarthy, Shakir Khan

et al.

Journal of Innovative Image Processing, Journal Year: 2025, Volume and Issue: 7(1), P. 146 - 160

Published: March 1, 2025

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

Language: Английский

Citations

0