DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification

Chughtai, Suhaib and Senousy, Zakaria and Mahany, Ahmed and Elmitwally, Nouh and Ismail, Khalid N. and Gaber, Mohamed Medhat and Abdelsamea, Mohammed M. (2024) DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification. IEEE Open Journal of the Computer Society. ISSN 2644-1268

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Abstract

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality. Precise diagnosis of CRC plays a crucial role in increasing patient survival rates and formulating effective treatment strategies. Deep learning algorithms have demonstrated remarkable proficiency in the precise categorization of histopathology images. In this paper, we introduce a novel deep learning model, termed DeepCon which incorporates the divide-and-conquer principle into the classification task. DeepCon has been methodically conceived to scrutinize the influence of acquired composition on the learning process, with a specific application to the classification of histology images related to CRC. Our model harnesses pre-trained networks to extract features from both the source and target domains, employing a two-stage transfer learning approach encompassing multiple loss functions. Our transfer learning strategy exploits a learned composition of decomposed images to enhance the transferability of extracted features. The efficacy of the proposed model was assessed using a clinically valid dataset of 5000 CRC images. The experimental results reveal that DeepCon when coupled with the Xception network as the backbone model and subjected to extensive fine-tuning, achieved a remarkable accuracy rate of 98.4% and an F1 score of 98.4%

Item Type:	Article
Identification Number:	10.1109/OJCS.2024.3428970
Dates:	Date Event 7 May 2024 Accepted 1 August 2024 Published Online
Uncontrolled Keywords:	class composition, data irregularity, deep learning, colorectal cancer, image classification
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Nouh Elmitwally
Date Deposited:	14 May 2024 13:50
Last Modified:	08 Aug 2024 13:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15481

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