Lightweight and Explainable Deep Learning Framework for Endoscopic Lesion Classification with Segmentation and Transfer Learning
TL;DR
This study integrates segmentation, transfer learning, and clinical validation to present a lightweight deep learning architecture for endoscopic lesion categorization that dramatically reduces latency and parameter count while achieving competitive accuracy when compared to more sophisticated models.
Abstract
This study integrates segmentation, transfer learning, and clinical validation to present a lightweight deep learning architecture for endoscopic lesion categorization. The Bionnica Lite architecture, a small convolutional neural network intended to provide a good classification performance with less computing cost, is at the center of the strategy. A segmentation module based on SAM 2.1 is included to improve lesion-focused analysis, allowing for accurate region-of-interest identification and better feature representation. Using pre-trained encoders such as EfficientNet-B0, the system supports both direct and transfer learning. According to experimental data, Bionnica Lite dramatically reduces latency and parameter count while achieving competitive accuracy when compared to more sophisticated models. Unlike existing studies that primarily emphasize predictive accuracy, the proposed framework investigates the balance between diagnostic performance, computational efficiency, lesion localization, and clinical interpretability within a unified deployment-oriented pipeline.