GabAI: Transfer Learning and Optimization Strategies for Lightweight CNNs in Mobile-Based Monkeypox Lesion Detection with Explainable AI

Abstract

Early and accurate diagnosis of monkeypox is critical, especially in resource-limited settings where access to laboratory diagnostics like PCR is constrained. This study explores the integration of transfer learning and optimization strategies using lightweight convolutional neural networks (CNNs), specifically MobileNetV2 and EfficientNetB0, for the classification of monkeypox, chickenpox, measles, and normal skin lesions. Multiple training configurations were implemented using two optimizers (Adam and SGD), two learning rates (0.001 and 0.0001), and four class imbalance handling strategies (none, class weights, oversampling, both). Results show that MobileNetV2 consistently outperformed EfficientNetB0, with feature extraction and class weights under Adam optimizer at a 0.001 learning rate achieving the highest accuracy (85.00%) and AUPRC (0.9284). Grad-CAM was integrated to enhance interpretability, offering real-time visual explanations of model predictions. The best-performing model was deployed in a React Native mobile application with a Flask backend, capable of real-time image classification and explainability. This study demonstrates the feasibility and clinical relevance of deploying interpretable, lightweight CNN models for mobile-based monkeypox diagnosis. The final application, GabAI: Grad-Aided Bioscan Intelligence, showcases how Explainable AI can be deployed in mobile platforms to support clinical decision-making.