Quantitative Evaluation of Repeatability in medical

Medical microwave imaging systems have shown significant potential in assisting healthcare professionals for medical image analysis and diagnostic support. This project presents an AI-assisted Microwave Imaging and Patient Management System developed using Django, Ollama, and the LLaVA multimodal model. The system provides an integrated platform for administrators, doctors, and patients to manage medical imaging workflows efficiently. In the proposed system, patients can upload X-ray images through a secure web interface, and assigned doctors can access these images for microwave-based image analysis. The uploaded X-ray images are processed using the LLaVA vision-language model integrated through Ollama, which dynamically generates intelligent microwave imaging reports based on the visual characteristics of the medical images. Doctors can review, edit, and send the generated reports to patients through the platform. Additionally, the system supports PDF report generation, doctor-patient communication through a modern chat interface, patient-doctor assignment management, and secure medical record handling. The proposed application aims to simplify medical imaging workflows, improve communication between doctors and patients, and provide AI-assisted diagnostic support in an efficient and user-friendly manner. This project demonstrates the integration of artificial intelligence and web technologies for developing a modern healthcare support system.

Keywords: Medical Microwave Imaging, Artificial Intelligence, Django Framework, Ollama, LLaVA, X-ray Image Analysis, Patient Management System, Medical Image Processing, AI-assisted Diagnosis, Healthcare Web Application, Medical Report Generation, Vision-Language Model, OpenCV, Doctor-Patient Communication, PDF Report System.