Development of AI-Based Diagnostic Systems for Wound Images

Recent advancements in AI technology are revolutionizing the medical field by enhancing diagnostic accuracy, optimizing treatment plans, and providing personalized medical services. AI-based algorithms are particularly effective in analyzing medical images to detect diseases early. Telemedicine, which allows remote medical consultations, benefits from these techniques by improving access to healthcare and enabling accurate diagnoses through efficient data exchange. This work developed an AI-based diagnostic system using a Convolutional Neural Network (CNN) model to analyze wound images. The wound dataset from Kaggle, consisting of 377 images across seven categories, was augmented 25 times to improve model performance. The app allows users to take or upload wound images, which are then classified by the model, providing treatment and aftercare information. This app achieved a high accuracy of 95.716% in classifying wounds and provides basic treatment suggestions. Despite challenges like limited dataset size, the project successfully demonstrated the potential for real-time wound analysis in telemedicine and healthcare. Future improvements should include a more diverse dataset and advanced treatment recommendations to enhance the app's utility.