A Survey on Lung Cancer Detection and Location from CT Scan Using Image Segmentation and CNN

In the contemporary era, it is clear that lung cancer has always been the major cause of cancer-related patients’ death. Currently, the primary method to diagnose lung cancer in a suspected patient is to examine the CT scan images of the patient’s lungs. Therefore, determining it as soon as possible is the greatest strategy to prevent death. Most of the time, radiologists have a limited amount of time to examine and interpret medical images. Nevertheless, as medical technology advances, more and more imaging data is being generated. Deep learning and machine learning technologies are effective for automating the interpretation and diagnosis of medical images. In this, we used biopsy reports (symptoms including hoarseness, coughing, smoking addiction, yellow fingers, anxiety, fatigue, allergy, wheezing, alcohol consumption, breathing problem) displayed in patients’, ct scans to find out the location and size of the nodule as well as the stage of lung cancer. We built an interface that requires uploading a CT scan image and selecting symptoms. In logic view, we use segmentation and a CNN model to analyze the CT scan result in order to build a patient’s medical profile. Using these sophisticated models increases the ability to forecast. The majority of the image analysis effort is automated through the use of machine learning models with also including deep learning models as well. Medical professionals can then provide prophylaxis more quickly.