KDLight: A Lightweight Knowledge Distillation Framework for Medical Image Classification

Conventional standalone approaches for diagnosing individual diseases often fail to achieve robust generalization because they are severely impacted by overfitting. This results in poor adaptability to diverse image representations and an inability to balance performance with computational efficiency. In this study, we propose KDLight, a lightweight, novel CNN model designed for efficient medical image classification across diverse modalities, including MRI, X-ray, radiography, skin images, and histopathology. We employ Knowledge Distillation (KD), where insights from an efficient teacher model (MobileNet) guide the learning process of the KDLight student model. The KDLight model minimizes the number of parameters while enhancing feature learning across diverse medical image representations. Experimental results show that KDLight achieves 95.55% classification accuracy with only 2.96 seconds and a compact 7.5 MB disk size, significantly reducing parameter size, accelerating inference, and lowering computational costs compared to traditional pre-trained models. Additionally, KDLight ability to efficiently learn diverse image representations can be extended to other domains, such as crack classification (e.g., road, window, and building cracks), enabling high-performance detection across different surface defect categories.