Efficient and Interpretable Otoscopic Image Classification via Distilled CNN With Adaptive Channel Attention
Year: 2025
Type: article
Abstract: Accurate classification of otoscopic ear images is crucial for early diagnosis of ear pathologies such as Chronic Otitis Media, Earwax Plug, and Myringosclerosis. In this study, we propose a novel deep learning framework that employs a knowledge distillation strategy, wherein a high-capacity pre-trained teacher model (Vision Transformer or ResNet101) transfers learned representations to a lightweight student CNN model (EfficientNet-B0). The student network is further enhanced through the integration of an Adaptive Channel Attention (ACA) module, which selectively emphasizes informative features via channel-wise recalibration. The multi-scale feature distillation from the teacher improves generalization while the ACA block boosts sensitivity to clinically relevant regions. We validated our method on a publicly available otoscopic image dataset comprising four balanced classes. Our approach achieved an overall accuracy of 98.75%, with class-wise AUC values of 1.000 (CSOM), 1.000 (Earwax Plug), 0.994 (Myringosclerosis), and 0.992 (Normal), and a micro-average AUC of 0.997. Additionally, Grad-CAM analysis confirmed the model’s focus on diagnostically meaningful areas, supporting the interpretability of the predictions. These results demonstrate the effectiveness of our distillation-based ACA-enhanced architecture in otoscopic image classification, with potential to assist clinical decision-making in primary care and telemedicine applications.
Source: IEEE Access
Institutions: Multimedia University, KPJ Healthcare University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Funder: Multimedia University
OpenAlex ID: W4413104943