Preprint Article Version 1 This version is not peer-reviewed

Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features

Version 1 : Received: 12 September 2019 / Approved: 14 September 2019 / Online: 14 September 2019 (18:25:45 CEST)

How to cite: 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. Preprints 2019, 2019090139 (doi: 10.20944/preprints201909.0139.v1). 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. Preprints 2019, 2019090139 (doi: 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.

Subject Areas

computer-aided detection (CAD) system\and computerized tomography (CT) scan; acquisition; segmentation; Classification and Principal Components Analysis (PCA)

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