Quad-YOLOv5: Improved YOLOv5 for Liver Lesion Detection on Bio Medical CT Images

Abstract

The liver is an important organ in the human body, performing numerous vital functions that are essential for overall health and well-being. Accurately identifying lesions in medical CT scans has long been one of the most difficult problems in the field of medical image analysis. Appropriate treatment and management strategies can be implemented to address the underlying liver condition and optimize patient outcomes. The proposed method for Liver Lesion Detection, utilizes the DeepLeison dataset which contains many biomedical CT scan images with a variety of liver pathologies. The method proposes Quad-YOLOv5 which is based on popular driven object detection deep learning model called YOLOV5 (You Only Look Once) model. We build up the medical image dataset of Liver Lesion by collecting 6335 CT images by augmentation. To enhance the performance of the Quad-YOLOv5 model, we have implemented data augmentation techniques and conducted extensive experiments using the DeepLeison dataset. Our findings demonstrate that the model exhibits strong performance coupled with remarkable interpretability. Through meticulous experimentation, we have refined the model's capabilities, ensuring that it delivers superior results in lesion detection tasks while maintaining a high level of interpretability. Our model is trained and evaluated based on its performance. The precision and recall for the model are 96% and 93%.  It is obtained that Quad-YOLOv5 model is with Mean Average Precision (mAP50) of 97%. The model increases the efficiency and accuracy of diagnosing and treating liver lesions. It can be incorporated into existing clinical workflows to aid radiologists in the interpretation of CT scans.