Improving Early Diagnosis: The Intersection of Lean Healthcare and Computer Vision in Cancer Detection

Cancer is a relentless adversary, and late detection only grants it more time to tighten its grip on lives and futures, underscoring the critical importance of early diagnosis. The integration of Lean Healthcare principles and Computer Vision technology is reshaping the landscape of cancer detection and diagnosis. This approach combines Lean’s focus on efficiency and process optimization with Computer Vision’s ability to analyze medical images, leading to more accurate, timely, and patient-centric cancer detection methods. This study embarks on a comprehensive investigation, utilizing the YOLO (You Only Look Once) v7 network for cancer detection and harnessing the PanNuke dataset—an extensive repository featuring 205,343 nuclei spanning 19 distinct tissue types. Concurrently, it delves into the discourse surrounding the integration of Lean Healthcare practices, illuminating a pathway to enhance the efficiency of cancer detection through the collaborative utilization of computer vision models and Lean Healthcare principles. By using the YOLO general-purpose object detection algorithm, potential cancer cases are identified successfully. The model achieves a high detection accuracy of 91.76%, Confirming that computer vision algorithms are a dependable tool for expediting cancer detection with high accuracy.