Classification and Segmentation of Pulmonary Lesions in CT images using a combined VGG-XGBoost method, and an integrated Fuzzy Clustering-Level Set technique

• URL: http://arxiv.org/abs/2101.00948v1
• Date: Mon, 4 Jan 2021 13:25:13 GMT
• Title: Classification and Segmentation of Pulmonary Lesions in CT images using a combined VGG-XGBoost method, and an integrated Fuzzy Clustering-Level Set technique
• Authors: Niloofar Akhavan Javan, Ali Jebreili, Babak Mozafari, Morteza Hosseinioun
• Abstract summary: Current pulmonary disease diagnosis is made by human resources, which is time-consuming and requires a specialist in this field. Our goal is to develop a system that can detect and classify lung lesions with high accuracy and segment them in CT-scan images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Given that lung cancer is one of the deadliest diseases, and many die from the disease every year, early detection and diagnosis of this disease are valuable, preventing cancer from growing and spreading. So if cancer is diagnosed in the early stage, the patient's life will be saved. However, the current pulmonary disease diagnosis is made by human resources, which is time-consuming and requires a specialist in this field. Also, there is a high level of errors in human diagnosis. Our goal is to develop a system that can detect and classify lung lesions with high accuracy and segment them in CT-scan images. In the proposed method, first, features are extracted automatically from the CT-scan image; then, the extracted features are classified by Ensemble Gradient Boosting methods. Finally, if there is a lesion in the CT-scan image, using a hybrid method based on [1], including Fuzzy Clustering and Level Set, the lesion is segmented. We collected a dataset, including CT-scan images of pulmonary lesions. The target community was the patients in Mashhad. The collected samples were then tagged by a specialist. We used this dataset for training and testing our models. Finally, we were able to achieve an accuracy of 96% for this dataset. This system can help physicians to diagnose pulmonary lesions and prevent possible mistakes.

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