Advancement In Early Detection of Lung Cancer Using YOLOv7

Cancer, a word that instills fear and sorrow in the hearts of many, is a disease that wreaks havoc on the human body. Among the most lethal forms of cancer are lung, colorectal, breast, pancreatic, and prostate cancers. Smoking stands as the leading cause of lung cancer, a formidable adversary. However, the traditional diagnostic methods have proven to be inadequate and require an array of tests. In recent years, the integration of cutting-edge image processing and real-time object detection technologies has significantly improved the accuracy of diagnosing Pulmonary carcinoma. The emergence of highly advanced object detection systems has been observed globally that offer superior performance, accuracy, and cost-effectiveness. The utilization of YOLOv7 for detecting cancerous nodules in CT scans has emerged as the gold standard in lung cancer diagnosis. YOLOv7 operates as an algorithm for real-time object recognition that outperforms its predecessors, such as YOLOv2, YOLOv3, YOLOv4, and YOLOv5, all while remaining cost-effective. $\mathrm{YOLO}^{2} 7$ excels at identifying even the most minuscule lesions in lung images, providing hope for early detection and intervention with speed including precision 66 percent and recall 83 percent. Machine learning-based image processing and object detection technologies have found extensive applications in the medical field, revolutionizing how we approach disease diagnosis and treatment.