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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1431912

Advanced CNN Models in Gastric Cancer Diagnosis: Enhancing Endoscopic Image Analysis with Deep Transfer Learning"

Provisionally accepted
Priya Bhardwaj Priya Bhardwaj 1Apeksha Koul Apeksha Koul 2Yogesh Kumar Yogesh Kumar 3*SeongKi Kim SeongKi Kim 4Ankur Changela Ankur Changela 3Jana Shafi Jana Shafi 5Muhammad Fazal Ijaz Muhammad Fazal Ijaz 6*
  • 1 Tula's Institute, Dehradun, Uttarakhand, India
  • 2 Punjabi University, Patiala, Punjab, India
  • 3 Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
  • 4 Chosun University, Gwangju, Republic of Korea
  • 5 Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
  • 6 Melbourne Institute of Technology, Melbourne, Australia

The final, formatted version of the article will be published soon.

    The rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer. This research aims to utilize endoscopic images to identify various gastric disorders using an advanced Convolutional Neural Network (CNN) model. The Kvasir dataset, comprising images of normal Z-line, normal pylorus, ulcerative colitis, stool, and polyps, was used. Images were pre-processed and graphically analyzed to understand pixel intensity patterns, followed by feature extraction using adaptive thresholding and contour analysis for morphological values. Five deep transfer learning models-NASNetMobile, EfficientNetB5, EfficientNetB6, InceptionV3, DenseNet169-and a hybrid model combining EfficientNetB6 and DenseNet169 were evaluated using various performance metrics. For the complete images of gastric cancer, EfficientNetB6 computed the top performance with 99.88% accuracy on a loss of 0.049. Additionally, InceptionV3 achieved the highest testing accuracy of 97.94% for detecting normal pylorus, while EfficientNetB6 excelled in detecting ulcerative colitis and normal Z-line with accuracies of 98.8% and 97.85%, respectively. EfficientNetB5 performed best for polyps and stool with accuracies of 98.40% and 96.86%, respectively. The study demonstrates that deep transfer learning techniques can effectively predict and classify different types of gastric cancer at early stages, aiding experts in diagnosis and detection.

    Keywords: gastric cancer, medical images, deep learning, ulcerative colitis, Transfer Learning, Contour features

    Received: 14 May 2024; Accepted: 09 Aug 2024.

    Copyright: © 2024 Bhardwaj, Koul, Kumar, Kim, Changela, Shafi and Ijaz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yogesh Kumar, Pandit Deendayal Energy University, Gandhinagar, 382 007, Gujarat, India
    Muhammad Fazal Ijaz, Melbourne Institute of Technology, Melbourne, Australia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.