Analysis based on machine and deep learning techniques for the accurate detection of lung nodules from CT images

Abstract

The imaging modality of computed tomography (CT) is significant for the diagnosis of lung cancer. Doctors, surgeons, radiologists, and oncologists choose CT scanning to find these nodules for diagnosing lung cancer early by using computer-aided diagnosis tools. Early malignant diagnosis is necessary for providing an efficient treatment for patients who are affected by lung cancer and there are multiple methods available for the detection of these modules. Gaining knowledge about the various methods is more important for developing a novel model and hence in this research, 50 recent articles from the last five years are evaluated with a focus on lung nodule identification and a goal of highlighting the problems associated with the current approaches. Based on several variables, including performance measures, publication years and journals, achievements of the methods in numerical evaluations, and other things, the evaluation of various methods is made in this study. On the contrary, a technical analysis that considers the advantages and disadvantages of the approach is also interpreted. This study analyzed several deep learning and related techniques for locating lung nodules using CT scans that provide a deeper insight into their cons and pros. The investigation revealed that the deep learning optimization-based strategies outperformed the convolutional techniques concerning the performance measures like accuracy, sensitivity, specificity, F-score, precision, and recall for the achievements, which vary from 80 to 100%. Lastly, the report examines potential directions for future research as well as challenges to improving lung nodule detection accuracy.