Team Members: Dr.M.Krishnaveni, Assistant Professor(SG), Department of Computer Science

Dr.P.Narendran , Gobi Arts and Science College

Ms.S.Aiswarya, Research Scholar , Department of Computer Science

Ms.Aathi Obusre M , II MCA , Gobi Arts and Science College

Project Summary :Sickle Cell Disease (SCD) is a genetic blood disorder characterized by the presence of abnormal hemoglobin S (HbS), which leads to hemolysis and chronic organ damage. Previous research has primarily focused on classification, whereas this work proposes detection and cell counting methodologies to determine the severity of sickle cell disease.This study effectively addresses the challenges of evaluating sickle cell disease severity through quantitative analysis of cell counts within images, thereby providing valuable data for understanding the condition. Using deep learning for object detection, single stage detectors outperforms in detection accuracy with inference time and it is more effective . In this erythrocyteIDB dataset is used for detection to enhance the training dataset the data augmentation techniques are employed to expand the data. The object detection task in this study utilizes YOLOv4, YOLOv5, and YOLOv8 models.  Comparing these three models YOLOv8 gives better results with accuracy. Intersection over union (IOU) and Non-Maximum Suppression (NMS) algorithms are applied to eliminate duplicate detections and prevent overlapping bounding boxes. The results shows that YOLOv8 exhibits an accuracy of 0.83 at mean average value precision. From the analysis, the proposed model successfully recognizes and counts the different types of cells present in the blood smear image.