Endoscopic image analysis using Deep Convolutional GAN and traditional data

Auzine, Muhammad, Khan, Maleika Heenaye-Mamode, Baichoo, Sunilduth, Gooda Sahib, Nuzhah, Gao, Xiaohong W. ORCID logoORCID: https://orcid.org/0000-0002-8103-6624 and Bissoonauth-Daiboo, Preeti (2022) Endoscopic image analysis using Deep Convolutional GAN and traditional data. 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). In: International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 16-18 Nov 2022, Maldives. . [Conference or Workshop Item] (Accepted/In press)

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Abstract

One big challenge encountered in the medical field is the availability of only limited annotated datasets for research. On the other hand, medical image annotation requires a lot of input from medical experts. It is noticed that machine learning and deep learning are producing better results in the area of image classification. However, these techniques require large training datasets, which is the major concern for medical image processing. Another issue is the unbalanced nature of the different classes of data, leading to the under-representation of some classes. Data augmentation has emerged as a good technique to deal with these challenges. In this work, we have applied traditional data augmentation and Generative Adversarial Network (GAN) on endoscopic esophagus images to increase the number of images for the training datasets. Eventually we have applied two deep learning models namely ResNet50 and VGG16 to extract and represent the relevant cancer features. The results show that the accuracy of the model increases with data augmentation and GAN. In fact, GAN has achieved the highest accuracy, that is, 94% over non-augmented training set and traditional data augmentation for VGG16.

Item Type: Conference or Workshop Item (Presentation)
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Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 36335
Notes on copyright: Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Xiaohong Gao
Date Deposited: 30 Sep 2022 12:54
Last Modified: 13 Dec 2022 20:24
URI: https://eprints.mdx.ac.uk/id/eprint/36335

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