Integrating Yolo V5 Analysis and KNN to Improve Lung Cancer Detection

Abstract

Lung cancer is a leading cause of cancer-related mortality globally, emphasizing the urgent need for early detection and accurate diagnosis. This project aims to leverage advanced deep learning techniques, specifically YOLO-v5 (You Only Look Once) for object detection, and the k-Nearest Neighbors (kNN) algorithm for unsupervised learning, to enhance the detection and analysis of lung cancer from CT scan images. YOLO-v5, known for its exceptional speed and accuracy in detecting objects within images, will be used to identify and localize lung nodules, which are potential indicators of lung cancer. Simultaneously, we will employ the kNN algorithm in a novel application of unsupervised learning to cluster CT scan images based on the similarity of detected lung tumors, enabling the identification of patterns and characteristics that may correlate with specific types of lung cancer. This project involves collecting and preprocessing a diverse dataset of CT images annotated with radiologist insights to train the YOLO-v5 model. Subsequently, the kNN algorithm will be applied to perform clustering on the detected tumors. By achieving high accuracy in nodule detection and effectively clustering similar tumors, the system aims to become an invaluable tool for radiologists, providing rapid diagnostic assistance and facilitating a deeper understanding of lung cancer characteristics.