Lung Cancer Detection using Fusion and YOLO Techniques

Lung cancer is one of the major causes of death in India. Various data analytics and classification approaches have been used to diagnose and find lung cancer in numerous cases. Lung cancer can only be cured by early tumor diagnosis because the basis of the disease is yet unknown, making prevention impossible. In order to classify the existence of lung cancer/tumor in a CT-picture and PET image, a lung cancer detection method using image processing and deep learning is applied. Using fusion techniques, we first obtain CT scans, then PET images, of the same patient, then combine both images into one. classification carried out using image feature extraction. As a result, the combined CT and PET scan images of the patient are classified as normal and abnormal. The tumor component of the abnormal photos is the focus of the detecting process. Using YOLO and CNN, an effective strategy to identify lung cancer and its phases is one that also seeks to produce more precise results.

Keywords: Lung Cancer/Tumor, CT (Computed Tomography), PET (Positron Emission Tomography), Fusion Techniques, Detection, CNN (Convolutional Neural Networks), YOLO (You Only Look Once) V2.