Smart Detection of Ear Infection using Artificial Intelligence with Deep Learning

Ear infections are among the most prevalent health conditions, especially in children, and typically require expert clinical interpretation to be diagnosed reliably using otoscopy photographs. This work overcomes the limitation of expertise-based and variable diagnosis by suggesting a deep learning-based intelligent detection system optimized for automatic ear infection classification. The aim is to develop a strong, efficient, and scalable solution that can be executed in real-time and resource-constrained settings. The system consists of advanced preprocessing using Wiener filtering, DW-Net-based segmentation for providing accurate localization of infected regions, and EfficientNet-based feature extraction. A new fuzzy Restricted Boltzmann Machine, fine-tuned specifically using Bayesian optimization, is tasked with conducting the classification, thus ensuring interpretability of the model and correctness of the decision. The modular architecture, developed using Python and recent web technology, is compatible with offline execution and allows for multilingual deployment of telemedicine. The novelty in the system lies in its hybrid architecture that fuses state-of-the-art convolutional neural networks with fuzzy logic and optimization to deliver ethical, accessible AI-based diagnostics, thus reducing dependence on medical experts significantly.