Advancements in Early Detection of Lung Cancer using YOLOv7

Abstract

Lung cancer, a leading cause of death worldwide, requires early detection for effective treatment. Traditional diagnostic methods often prove inadequate, highlighting the need for advanced techniques. This research explores the application of YOLOv7, a state-of-the-art object detection algorithm, for identifying malignant nodules in CT scans. By utilizing machine learning-based image processing and object detection, YOLOv7 demonstrates superior performance in accurately detecting even small lung nodules. Its efficiency and accuracy surpass previous methods, offering hope for early diagnosis and intervention. This research contributes to the advancement of lung cancer detection and highlights the potential of machine learning in revolutionizing medical imaging. By enabling earlier diagnosis, YOLOv7 can significantly improve patient outcomes and save lives.