An automated classification methodology of sub-centimeter pulmonary structures in computed tomography images. (June 2020)

• Record Type: Journal Article
• Authors: Mukherjee, Jhilam; Poddar, Triparna; Kar, Madhuchanda; Ganguli, Bhaswati; Chakrabarti, Amlan; Das, Sayan
• Is Part Of: Computers & electrical engineering. Volume 84(2020)
• Publication Date: 2020-06
• Subjects: Subcentimeter pulmonary nodule -- Thoracic CT -- Imbalance class -- Multi level thresholding -- Penalised multinomial classifier; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106629
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This paper proposes a novel prediction model for detection of sub-centimeter pulmonary nodules from thoracic computed tomography (CT) images. We perform multi-level thresholding for lesion segmentation followed by penalised multinomial classifier for identifying the lesion type. We have considered 17, 893 sub-centimeter pulmonary structures from an independent dataset collected retrospectively from Peerless Hospital, Kolkata to develop the classification model. An important feature of this dataset is class imbalance i.e. pulmonary vessels account for 70% of the structures while bones, scars, and nodules respectively account for 2%, 5%, and 23%. To the best of our knowledge, only a few researchers have addressed the issue of imbalanced classes for lung computed tomography (CT) images. The specificity, sensitivity and misclassification rates indicate that the method has the potential to reduce the number of undetected early stage lesions.