Please use this identifier to cite or link to this item: http://prr.hec.gov.pk/jspui/handle/123456789/15122
Title: Intelligent Detection and Classification of Lung Nodules from CT Images
Authors: Naqi, Syed Muhammad
Keywords: Physical Sciences
Computer Science
Issue Date: 2020
Publisher: COMSATS University, Islamabad.
Abstract: Intelligent Detection and Classification of Lung Nodules from CT Images Lung cancer is considered as the most probable cancer in human beings. Moreover, the survival rate of its patients is also very low. However, its early detection increases the chances of survival of its patients. Medical imaging helps in early diagnosis, and the most effective method is Computed Tomography (CT). The initial appearance of lung cancer is the presence of a nodule in the lungs. On a CT scan, a lung nodule appears as a round object, either independent (isolated), pleura-attached (juxtapleural) or vessel-attached (juxta-vascular). Computer-based systems help the radiologists in the diagnosis process by providing the second opinion. However, there is a significant challenge of sensitivity and the number of false positives in the existing techniques. The main objective of this research is to develop algorithms/methods which detect and classify lung nodules precisely. Four methods are proposed, involving lung volume extraction, nodule candidate detection, feature extraction, and classification. Lung volume extraction is computed by using the optimal threshold. Fractional order Darwinian particle swarm optimization is applied for threshold selection, which significantly improves the segmentation of lung volume. Nodule candidate detection is performed by applying 3D image analysis. Precise boundary correction and 3D segmentation are proposed for the detection of juxta-pleural and juxta-vascular nodules, respectively. Nodule candidates are refined using their shape properties. A set of 2D, as well as 3D features, including geometric, texture, and gradient features, are extracted with variations. Hybrid feature vectors are created by the combination and selection of relevant features to improve classification. The initial classification is performed by k-NN, SVM, Naïve Bayes and AdaBoost classifiers. In addition, an SVM-ensemble classifier is implemented. Furthermore, a stacked autoencoder and softmax is applied to reduce the false positives. The proposed methods are evaluated over publicly available LIDC dataset, and the largest benchmark dataset LIDC-IDRI. The first method achieved an accuracy of xi 98.8%, 97.8% sensitivity and 3.7 False Positives (FP) per scan. The second method achieved an accuracy of 99.2% with 98.3% sensitivity, 98.0% specificity and 3.3 FPs/scan. The third method achieved 99.0% accuracy, 98.6% sensitivity, 98.2% specificity with 3.4 FPs/scan. The fourth method achieved 96.9% accuracy, 95.6% sensitivity, 97.0% specificity and very low false positives of 2.8/scan. The results are compared with the existing methods over standard parameters, including sensitivity, specificity, accuracy and FPs/scan. The comparison is also extended to other parameters, including the number of scans, number of nodules and size of nodules used for experimentation. The comparison points to the significance of this research. The methods presented in this thesis will be useful for the radiologists in automated diagnosis of lung nodules at the earlier stage.
Gov't Doc #: 20318
URI: http://prr.hec.gov.pk/jspui/handle/123456789/15122
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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