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Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection
Author(s) -
Jena Sanjukta Rani,
George Selvaraj Thomas
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22445
Subject(s) - artificial intelligence , pattern recognition (psychology) , convolutional neural network , computer science , feature extraction , preprocessor , segmentation , kernel (algebra) , lung cancer , image segmentation , feature (linguistics) , contextual image classification , mathematics , image (mathematics) , medicine , pathology , linguistics , philosophy , combinatorics
Lung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non‐Gaussian Convolutional Neural Network (KNG‐CNN). KNG‐CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non‐Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC‐IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG‐CNN method is used for effectual classification of tumour > 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.