MedSignNet: Scalable Deep Learning Solutions for Multi-Class Medical Symptom Recognition in Healthcare

Abstract

The automatic interpretation of medical symptoms from visual inputs is gaining momentum as a promising tool to support telemedicine and clinical decisionmaking. This study introduces a deep learning framework designed for the dual task of medical symptom classification and medical sign language recognition, covering ten symptom categories: Headache, Allergy, Smallpox, Diarrhea, Fever, Infection, Injection, Pain, Emergency, and Vomit. The framework is built on an EfficientNet-B0 backbone, adapted through transfer learning and strengthened with augmentation techniques such as mix-up, random flipping, and cosine annealing for learning rate adjustment. Training was carried out in two stages: first, by freezing the backbone and refining the classifier head, and then by fine-tuning the complete model with label smoothing. To overcome data imbalance, a weighted sampling strategy was integrated alongside mix-up augmentation to encourage better generalization. The proposed approach achieved an accuracy of 96 % on the test set, with testtime augmentation further enhancing its robustness. The results highlight that compact yet efficient convolutional models can serve as practical and scalable solutions for healthcare applications involving medical image analysis and sign language recognition.

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