Intelligent Sickle Cell Disease Classification and Severity Assessment System Using YOLO with AI-Powered Recommendations

This project introduces an AI-based system for diagnosing and assessing the severity of Sickle Cell Disease (SCD), a haemolytic anaemia caused by abnormal haemoglobin in red blood cells. The system utilizes the YOLOv8 object detection model to accurately and efficiently detect and classify sickle cells in blood smear images. By integrating the Google Gemini API, the system provides AI-powered, patient-specific recommendations to support early interventions and improve healthcare outcomes. Trained on a mixed dataset of blood smear images, the model identifies and quantifies sickle cells, enabling the calculation of disease severity based on the percentage of affected cells. This approach offers a faster, more accurate alternative to traditional diagnostic methods, reducing errors and alleviating the workload of healthcare providers. Preliminary results demonstrate significant improvements in diagnostic accuracy and clinical decision-making efficiency. The system aims to revolutionize patient care by enhancing early diagnosis, providing personalized treatment recommendations, and improving overall clinical outcomes. Future goals include enhancing the AI's performance and expanding the dataset for more robust diagnostics.