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ColoXAI-RecomNet: Explainable Recommender Framework for Colorectal Cancer Classification Using Integrated CNN
Akella S Narasimha Raju1, Ranjith Kumar Gatla2, G Sucharitha3
1Department of Computing Technologies, School of Computing, College of Engineering & Technology, SRM Institute of Science and Technology. Kattankulathur, Chennai, Tamil Nadu, 603203, India. akellar@srmist.edu.in.
This study introduces ColoXAI-RecomNet, an explainable framework for classifying colorectal diseases from colonoscopy images with high accuracy. The best model achieved 98.60% accuracy, offering interpretable decision support for clinical use.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Pathology
Background:
- Accurate classification of colorectal diseases from colonoscopy images is crucial.
- Existing methods often lack interpretability, hindering clinical adoption.
- Explainable AI (XAI) offers a path towards trustworthy diagnostic tools.
Purpose of the Study:
- To propose and evaluate a five-stage explainable framework (ColoXAI-RecomNet) for multi-class colorectal image classification.
- To enhance predictive accuracy and provide clinically interpretable decision support.
- To leverage deep learning and machine learning for robust colorectal disease detection.
Main Methods:
- Investigated three parallel hybrid Convolutional Neural Network (CNN) ensemble models (RDV-2025, IEM-2025, DRE-2025) as feature extractors.
- Employed XGBoost for feature refinement (Stage 2) and a multi-class Support Vector Machine (SVM) with an RBF kernel for classification (Stage 3).
- Integrated LIME for visual explanations (Stage 4) and converted outputs to a clinically interpretable recommender format (Stage 5).
Main Results:
- The DRE-2025 CNN ensemble model showed strong performance in initial feature extraction.
- The combined DRE-2025 + XGBoost + SVM model achieved a high accuracy of 98.60%.
- The framework demonstrated excellent performance metrics: Precision (98.75%), Recall (98.50%), F1-score (98.62%), and AUC (99.30%).
Conclusions:
- The proposed ColoXAI-RecomNet framework effectively classifies colorectal diseases from colonoscopy images with high accuracy and interpretability.
- The integration of CNNs, XGBoost, SVM, and LIME provides a powerful and explainable approach for medical image analysis.
- This framework holds significant potential for improving diagnostic accuracy and clinical decision-making in gastroenterology.
