Deep Neural Network Based Automatic Detection and Classification of Lung Nodules from CT images

Abstract

The main cause of mortality globally is cancer, with lung cancer being the most common among all cancer types. Radiologists use computer tomography (CT) scans to detect and monitor cancer in the body. Due to subjectivity, visual complexity, and wide variances among interpreters, human professionals ability to interpret images is relatively restricted. Consequently, early cancer detection using image processing techniques is possible. Image segmentation and the extraction of radiomic features from CT scans are the two main components of image processing. Deep learning is currently offering innovative, highly authentic solutions for medical imaging and is recognized as a crucial technique for upcoming applications in the health sector as a result of its success in other real-world applications. A a deep learning model along with image processing techniques is proposed for detecting lung cancer. SPIE-AAPM Lung CT Challenge dataset is used for this study. The proposed Deep Learning Model based Lung Cancer Detection (DLMLCD) achieves an accuracy of 99% on evaluation of the above dataset.