PathRes_FM: A regularized ResNet-50 framework for multi-class colorectal histopathology tissue classification
Accurate tissue-level classification in colorectal histopathology is important for developing reliable computer-aided pathology systems, particularly when multiple tissue categories show high morphological similarity. This study proposes PathRes_FM, a regularized ResNet-50–based deep learning framework for multi-class colorectal histopathology tissue classification using the PathMNIST benchmark dataset. The framework classifies nine colorectal tissue categories: adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. The proposed pipeline uses an ImageNet-pretrained ResNet-50 backbone with a task-specific dropout-regularized classification head. To improve generalization, the training strategy incorporates image augmentation, MixUp, CutMix, label smoothing, AdamW optimization, and OneCycleLR scheduling. Experimental evaluation demonstrated strong predictive performance on the PathMNIST benchmark. The proposed PathRes_FM model achieved an overall accuracy of 94.04%, an area under the receiver operating characteristic curve of 0.9890, a weighted F1-score of 0.9399, a weighted precision of 0.9410, and a weighted recall of 0.9404. Class-wise analysis showed strong performance for adipose, lymphocytes, mucus, normal colon mucosa, and colorectal adenocarcinoma epithelium, while cancer-associated stroma and smooth muscle remained comparatively challenging due to morphological overlap. Comparative evaluation further showed that PathRes_FM outperformed Auto-Sklearn, ResNet-18, and ResNet-50 baseline models on the same benchmark setting. Additional analyses using confusion matrix, receiver operating characteristic curves, precision–recall curves, t-distributed stochastic neighbor embedding visualization, calibration assessment, robustness plots, and gradient-weighted class activation mapping-style interpretation supported the discriminative and interpretable behavior of the learned representations. The results indicate that PathRes_FM is an effective supervised image-level framework for colorectal histopathology tissue classification and provides a strong baseline for future extensions toward whole-slide and pathology-specific representation-learning systems. This work explicitly positions PathRes_FM as a reproducible benchmark-level supervised transfer-learning framework rather than as a new whole-slide foundation model or clinical deployment system.
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