Title: Colorectal cancer classification based on deep ensemble model with self-adaptive training model

Authors: A. Karthikeyan; S. Jothilakshmi; S. Suthir

Addresses: Department of Information Technology, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu 608002, India ' Department of Information Technology, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu 608002, India ' Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu 601103, India

Abstract: The four-phase colorectal cancer classification using deep ensemble model (CCCDEM) was proposed in this work. Initially, the Weiner filtering is used to preprocess the input image. The second phase produces the segmented cancer region from the preprocessed image using FCM-based segmentation. ILDTP, LGP, and MBP are only a few of the features that are extracted during the third phase. The ensemble classification model, which combines the three classifiers DBN, DMN, and CNN, is then used in the fourth phase to provide the classified output. Here, the classification outcome is determined by the optimised CNN model that trains with the scores generated from the DBN, and DMN classifiers. Since the optimal training ensures accurate categorisation, this work seeks to provide a new self-adaptive Tasmanian devil optimisation algorithm (SATDOA) for training the model by setting best weights.

Keywords: colorectal cancer; clustering model; optimisation; features; deep learning.

DOI: 10.1504/IJBET.2024.138065

International Journal of Biomedical Engineering and Technology, 2024 Vol.44 No.4, pp.367 - 396

Received: 25 Apr 2023
Accepted: 20 Jun 2023
Published online: 18 Apr 2024 *

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