[LGPR] Publication Success Series- Dr Sheshang Degadwala

Dr Sheshang Degadwala, Sigma University Vadodara-India

Journal of Innovative Image Processing,7(1) 2025 | e-ISSN: 2582-4252

SCOPUS Source Link: https://www.scopus.com/sourceid/21101274722

Cite Score: 1.6

Journal of Innovative Image Processing, intends to provide an effective research interaction on the outcomes of high-quality theoretical and applied research from all the areas of image processing and analysis. JIIP publishes the research contributions that propose novel image interpretation and analysis methodology or the appropriate application of image processing methods in real-world settings.

The coverage of the Journal of Innovative Image Processing (JIIP) includes: image analysis, image coding and enhancement, image interpretation and retrieval, embedded image processing and real-time image processing applications, spectral imaging systems, medical imaging devices, pattern recognition, neural networks, software, specialized computer architectures and other related areas.

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Prostate Biopsy Image Gleason Grading Classification using Machine Learning https://doi.org/10.36548/jiip.2025.1.007

Author Details:

Sheshang Degadwala

Lincoln University College, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia

Divya Midhunchakkaravarthy

Lincoln University College, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia

Shakir Khan

College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh Saudi Arabia.

University Centre for Research and Development, Chandigarh University, Mohali, India

Abstract

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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.

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Indexing

Patents

PROSTATE CANCER DETECTION AI DEVICE