Meraj, T.; Hassan, A.; Zahoor, S.; Rauf, H.T.; Lali, M.; Ali, L.; Bukhari, S.A.C. Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features. Preprints2019, 2019090139. https://doi.org/10.20944/preprints201909.0139.v1
APA Style
Meraj, T., Hassan, A., Zahoor, S., Rauf, H.T., Lali, M., Ali, L., & Bukhari, S.A.C. (2019). Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features. Preprints. https://doi.org/10.20944/preprints201909.0139.v1
Chicago/Turabian Style
Meraj, T., Liaqat Ali and Syed Ahmad Chan Bukhari. 2019 "Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features" Preprints. https://doi.org/10.20944/preprints201909.0139.v1
Abstract
Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.
Keywords
computer-aided detection (CAD) system\and computerized tomography (CT) scan; acquisition; segmentation; Classification and Principal Components Analysis (PCA)
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
