Computer-Aided Detection (CAD) system for the Detection and Classification of Pulmonary Nodules in Lung Cancer using CT images

Computer-Aided Detection (CAD) system for the Detection and Classification of Pulmonary Nodules in Lung Cancer using CT images
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© 2023 by IJETT Journal
Volume-71 Issue-1
Year of Publication : 2023
Author : Aparna M. Harale, Vinayak K. Bairagi
DOI : 10.14445/22315381/IJETT-V71I1P204

How to Cite?

Aparna M. Harale, Vinayak K. Bairagi, "Computer-Aided Detection (CAD) system for the Detection and Classification of Pulmonary Nodules in Lung Cancer using CT images," International Journal of Engineering Trends and Technology, vol. 71, no. 1, pp. 31-40, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I1P204

Abstract
Lung cancer is one type of serious disorder around the globe. In comparison to other forms of cancer in both males and females, lung cancer records the highest number of cancer-related deaths. Pulmonary nodules are blob-like shapes that are potential manifestations of lung cancer and diameter between 3 to 30 mm. correct examinations of nodules are required for the lung cancer diagnosis and the subsequent treatment schedule. Lung cancer screening can significantly reduce the death rate. Novice radiologists rely on professional specialists to study lung CT images. The primary bottleneck for such a traditional learning system is a lack of time from professional radiologists. Hence, there is a requirement for the development of an Automatic Computer Aided Detection system (CAD) to assist radiologists and the analysis of lung cancer. Due to poor image quality that interferes with the segmentation process, traditional lung cancer prediction approaches could not maintain accuracy. In order to predict lung cancer, novel, improved image processing and machine learning technique is presented in this study. This paper aims to develop an Automatic CAD system to diagnose lung cancer. The Lung Image Database Consortium (LIDC) CT image was used. The detection of nodules is done using UNET architecture, and its malignancy is decided with an ensemble of three classifiers, SVM, KNN and LR are examined using Python. LR gives the highest accuracy, 97.92%. Performance check of different classifiers using Accuracy, Sensitivity, Specificity, Precision, and F1 Score. The ensemble of three classifiers model detects and classifies lung nodules with an accuracy of 83%.

Keywords
Computer tomography, Computer-aided diagnosis, Lung cancer, Nodule, Nodule detection.

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