Early Detection Of gallstones using machine learning techniques

Gallbladder diseases, including gallstones, cholecystitis, and carcinoma, are common medical conditions that can lead to severe complications if not diagnosed early. Early detection is crucial for effective treatment and prevention of further health issues. This study proposes a deep learning-based framework for the automatic detection of gallbladder diseases using ultrasound images. The system employs Convolutional Neural Networks (CNN), DenseNet, and ResNet architectures to classify ultrasound images into nine distinct categories, including Gallstones, Cholecystitis, Perforation, Polyps, and others, from the provided dataset. The system uses a user-friendly web-based interface developed with HTML, CSS, and JavaScript for the front-end and Python with Flask for the back-end. Users can upload gallbladder ultrasound images through the 'Input' module and receive the classification results in the 'Output' module, which provides suggestions and recommendations for further medical action based on the detected condition. Additionally, a chatbot powered by Gemini AI API offers interactive support and personalized guidance. The proposed framework aims to assist healthcare professionals in accurately diagnosing gallbladder diseases at an early stage, improving treatment outcomes and reducing the risks associated with undetected conditions. The system's modular design and scalability make it an effective tool for integrating AI into medical diagnostics.

Keywords: Gallbladder Diseases, Ultrasound Imaging, Machine Learning, Convolutional Neural Networks (CNN), DenseNet, ResNet, AI Diagnosis, Flask, Gemini AI, Predictive Healthcare, Medical AI, Early Detection, Gallstones, Cholecystitis, Carcinoma.