Deployment of Kidney Tumor Disease Object Detection Using CT-Scan with YOLOv5
Abstract
Image processing plays a crucial role in identifying kidney tumors through CT-Scan images. Object detection technology, particularly YOLO, stands out for its speed and accuracy in facilitating more detailed analysis. Using Flask as a web framework offers optimal responsiveness, providing adaptive ease of use, especially in medical image processing. Evaluation of the model shows impressive results, with a mean Average Precision (mAP) of 0.987 for the 'kidney tumor' label. Detection on public data demonstrated high performance with accuracy, precision, recall, and F1-Score of 98.56%, 98.66%, 99.66%, and 99.16%, respectively. This study also utilized clinical data comprising 62 CT-Scan images. Evaluation of the clinical data revealed that YOLOv5 produced an accurate detection model with accuracy, precision, recall, and F1-Score of 95.16%, 96.72%, 98.33%, and 97.52%, respectively. The research shows that both public and clinical data models can accurately detect kidney tumors based on CT-Scan images. The deployment process using the Flask web-based platform allows direct interaction with users through an intuitive interface, enabling users to upload their CT-Scan images and quickly obtain detection results. These test results provide evidence that object detection using YOLOv5 achieves high accuracy in detecting both public and clinical datasets.
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