Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network. (17th March 2022)

• Record Type: Journal Article
• Authors: Akilandeswari, A.; Sungeetha, D.; Joseph, Christeena; Thaiyalnayaki, K.; Baskaran, K.; Jothi Ramalingam, R.; Al-Lohedan, Hamad; Al-dhayan, Dhaifallah M.; Karnan, Muthusamy; Meansbo Hadish, Kibrom
• Other Names: Roy Arpita Academic Editor.
• Is Part Of: Evidence-based complementary and alternative medicine. Volume 2022(2022)
• Publication Date: 2022-03-17
• Subjects: Alternative medicine -- Periodicals; 615.505
• DOI: 10.1155/2022/3415603
• Languages: English
• ISSNs: 1741-427X

Abstract

Abstract : Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.