ENT INSIGHT: Automated Medical Conditions Detection in E.N.T Images Using Deep Learning Techniques

Abstract

This research introduces an innovative approach that integrates image processing and deep learning technologies to enhance the diagnostics of various Ear, Nose, and Throat (E.N.T.) medical conditions. The system automates the identification of three key disorders: sinusitis, cholesteatoma, and pharyngitis. By leveraging diverse datasets from public medical repositories and employing state-of-the-art deep learning frameworks such as TensorFlow, the study aims to improve diagnostic accuracy and efficiency. The methodology employs a hybrid approach combining custom and pre-trained deep learning models to analyse medical images, including X-rays and endoscopic visuals. The preliminary findings indicate the potential of this system to significantly enhance early disease detection, reduce diagnostic time, and assist clinicians in making informed decisions. Furthermore, the research contributes to global healthcare advancements by developing a scalable, accessible, and reliable diagnostic solution available via web and mobile applications for both clinical and educational purposes.