Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network

A Akilandeswari; D Sungeetha; Christeena Joseph; K Thaiyalnayaki; K Baskaran; R Jothi Ramalingam; Hamad Al-Lohedan; Dhaifallah M Al-Dhayan; Muthusamy Karnan; Kibrom Meansbo Hadish

doi:10.1155/2022/3415603

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network

Evid Based Complement Alternat Med. 2022 Mar 17:2022:3415603. doi: 10.1155/2022/3415603. eCollection 2022.

Affiliations

¹ Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Nagar, Thandalam, Chennai, India.
² Department of Electronics & Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
³ Department of Electronics & Communication Engineering, Chennai Institute of Technology, Chennai, India.
⁴ Surfactant Research Chair, Chemistry Department, College of Science, King Saud University, P.O. Box. 2455, Riyadh 11451, Saudi Arabia.
⁵ Grassland and Forage Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Chungcheongnam-do, Republic of Korea.
⁶ Faculty of Mechanical Engineering, AMIT Campus, Arba Minch University, Arba Minch, Ethiopia.

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.