A Multi-Modal Deep Learning Framework for Colorectal Pathology Diagnosis: Integrating Histological and Colonoscopy Data in a Pilot Study

Abstract

Colorectal diseases, including inflammatory conditions and neoplasms, require quick, accurate care to be effectively treated. Traditional diagnostic pipelines require extensive preparation and rely on separate, individual evaluations on histological images and colonoscopy footage, introducing possible variability and inefficiencies. This pilot study proposes a unified deep learning network that uses convolutional neural networks (CN N s) to classify both histopathological slides and colonoscopy video frames in one pipeline. The pipeline integrates class-balancing learning, robust augmentation, and calibration methods to ensure accurate results. Static colon histology images were taken from the PathMNIST dataset, and the lower gastrointestinal (colonoscopy) videos were drawn from the HyperKvasir dataset. The CNN architecture used was ResNet-50. This study demonstrates an interpretable and reproducible diagnostic pipeline that unifies multiple diagnostic modalities to advance and ease the detection of colorectal diseases.