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

Suhaib Chughtai, Zakaria Senousy, Ahmed Mahany, Nouh Elmitwally, Khalid N. Ismail, Mohamed Medhat Gaber, Mohammed M. Abdelsamea

    Research output: Contribution to journalArticlepeer-review

    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%
    Original languageEnglish
    JournalIEEE Open Journal of the Computer Society
    DOIs
    Publication statusAccepted/In press (AAM) - 7 May 2024

    Keywords

    • class composition
    • data irregularity
    • deep learning
    • colorectal cancer
    • image classification

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