Advanced CNN Modifications for Detection of Lung Cancer from CT Scan Images

Abstract

Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some
limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. Therefore, this work implements the advanced modifications in CNN model for the detection of lung cancer from chest scan images. The proposed CNN model is able to classify the benign and malignant i.e., normal, and cancerous with higher accuracy as compared to state-of-the-art machine learning approach called support vector machine (SVM) classifier. In addition, the obtained quality metrics discloses the superiority of proposed deep CNN model for assisting the expertise in an enhanced diagnosis.