Endoscopic image analysis using deep convolutional GAN and traditional data augmentation
Conference item
Auzine, M., Khan, M., Baichoo, S., Gooda Sahib, N., Gao, X. and Bissoonauth-Daiboo, P. 2022. Endoscopic image analysis using deep convolutional GAN and traditional data augmentation. International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). Maldives 16 - 18 Nov 2022 IEEE. https://doi.org/10.1109/ICECCME55909.2022.9988503
Title | Endoscopic image analysis using deep convolutional GAN and traditional data augmentation |
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Authors | Auzine, M., Khan, M., Baichoo, S., Gooda Sahib, N., Gao, X. and Bissoonauth-Daiboo, P. |
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. |
Sustainable Development Goals | 3 Good health and well-being |
Middlesex University Theme | Health & Wellbeing |
Conference | International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
Proceedings Title | 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
ISBN | |
Electronic | 9781665470957 |
Paperback | 9781665470964 |
Publisher | IEEE |
Publication dates | |
18 Nov 2022 | |
Online | 30 Dec 2022 |
Publication process dates | |
Deposited | 30 Sep 2022 |
Accepted | 01 Aug 2022 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICECCME55909.2022.9988503 |
Scopus EID | 2-s2.0-85146426259 |
Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/xpl/conhome/9987724/proceeding |
Language | English |
https://repository.mdx.ac.uk/item/8q055
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