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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

GastroNet: A Custom Deep Learning Approach for Classification of Anomalies in Gastrointestinal Endoscopy Images

Author(s): Anitha Mary. A.*, Peniel Winifred Raj A., C. Karthik and Aldrin Karunaharan

Volume 20, 2024

Published on: 18 October, 2023

Article ID: e060923220762 Pages: 14

DOI: 10.2174/1573405620666230906092310

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Abstract

Introduction: Among all cancer forms, gastrointestinal (GI) cancer is the most serious condition that spreads quickly and requires early detection. GI disorders claim the lives of up to nearly two million people worldwide. To lower the mortality rate from GI cancer, early detection is essential.

Methods: For the identification of GI illnesses, such as polyps, stomach ulcers, and bleeding, endoscopy is the gold standard in the medical imaging industry. The numerous images produced by endoscopy require an enormous amount of time for the specialist to diagnose the disease. It makes manual diagnosis difficult and has sparked research on automatic computer-based approaches to diagnose all the generated images quickly and accurately. AI-based algorithms have already been used in endoscopy images with promising outcomes and have enhanced disease identification and classification with precision. However, there are still a lot of issues to be solved, including figuring out potential biases in algorithms and improving interpretability and generalizability.

Results: The proposed GastroNet model creates a system for classifying digestive problems for the Kvasir Version 1 dataset. The framework consists of different CNN layers with multiple filters, and average max-pooling is used to extract image features. The optimization of network parameters is done using the Stochastic Gradient Descent (SGD) algorithm.

Conclusion: Finally, the robustness of the proposed model is compared with other state-of-the-art models like VGG 19, ResNet 50, Inception, and Xception in terms of evaluation metrics.

Keywords: Gastrointestinal disorder, Classification of anomalies, Convolution neural network, Custom model, Performance metrics, Robustness.

[1]
Hamashima C, Group SR, Guidelines GDS. Update version of the Japanese guidelines for gastric cancer screening. Jpn J Clin Oncol 2018; 48(7): 673-83.
[http://dx.doi.org/10.1093/jjco/hyy077]
[2]
World Health Organization - International Agency for Research on Cancer. GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence Worldwide in 2012. 2012. Available from:https://publications.iarc.fr/Databases/Iarc-Cancerbases/GLOBOCAN-2012-Estimated-Cancer-Incidence-Mortality-And-Prevalence-Worldwide-In-2012-V1.0-2012
[3]
van Doorn SC, Hazewinkel Y, East JE, et al. Polyp morphology: An interobserver evaluation for the Paris classification among international experts. Am J Gastroenterol 2015; 110(1): 180-7.
[http://dx.doi.org/10.1038/ajg.2014.326] [PMID: 25331346]
[4]
Wang P, Berzin TM, Glissen JR. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study. Gut 2019; 68(10): 1813-9.
[5]
Liu JB, Pan XF. A unified approach to the asymptotic topological indices of various lattices. Appl Math Comput 2015; 270: 62-73.
[http://dx.doi.org/10.1016/j.amc.2015.08.008]
[6]
Le Berre C, Sandborn WJ, Aridhi S, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020; 158(1): 76-94.e2.
[http://dx.doi.org/10.1053/j.gastro.2019.08.058] [PMID: 31593701]
[7]
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019; 17(1): 195.
[http://dx.doi.org/10.1186/s12916-019-1426-2] [PMID: 31665002]
[8]
Attique Khan M, Mashood Nasir I, Sharif M, et al. A blockchain based framework for stomach abnormalities recognition. Comput Mater Continua 2021; 67(1): 141-58.
[http://dx.doi.org/10.32604/cmc.2021.013217]
[9]
Min M, Su S, He W, Bi Y, Ma Z, Liu Y. Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology. Sci Rep 2019; 9(1): 2881-8.
[http://dx.doi.org/10.1038/s41598-019-39416-7] [PMID: 30814661]
[10]
Wan QS, Wang T, Zhang KH. Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms. Tumour Biol 2017; 39(7): 1010428317717984.
[http://dx.doi.org/10.1177/1010428317717984] [PMID: 28671054]
[11]
Ribeiro AU, Häfner M. Colonic polyp classification with convolutional neural networks. Proceedings of the 2016 IEEE 29 International Symposium on Computer-Based Medical Systems (CBMS). Belfast and Dublin, Ireland. 2016; pp. 20-24 June; 253-8.
[http://dx.doi.org/10.1109/CBMS.2016.39]
[12]
Woreta SA, Yassin MO, Teklie SY, Getahun GM, Abubeker ZA. Upper gastrointestinal endoscopy findings at Gondar university international journal of pharmaceuticals and health care research. Int J Pharmaceut Health Care Res 2015; 3(2): 60-5.
[13]
Zhang X, Chen F, Yu T, et al. Real-time gastric polyp detection using convolutional neural networks. PLoS One 2019; 14(3): e0214133.
[http://dx.doi.org/10.1371/journal.pone.0214133] [PMID: 30908513]
[14]
Ozawa T, Ishihara S, Fujishiro M, Kumagai Y, Shichijo S, Tada T. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol 2020; 13: 1756284820910659.
[http://dx.doi.org/10.1177/1756284820910659] [PMID: 32231710]
[15]
Song EM, Park B, Ha CA, et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep 2020; 10(1): 30-10.
[http://dx.doi.org/10.1038/s41598-019-56697-0] [PMID: 31913337]
[16]
Sharif M.; Khan M.A.; Rashid M., et al. Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. J Exper Theoretical Artificial Intell 2021; 33(4): 577-99.
[http://dx.doi.org/10.1080/0952813X.2019.1572657]
[17]
Naz J, Alhaisoni M, Song O-Y, Tariq U, Kadry S. Segmentation and classification of stomach abnormalities using deep learning. CMC-Comput. Mater Contin 2021; 69: 607-25.
[18]
Wu J. Introduction to convolutional neural networks. 2017. Available from:https://developer.ibm.com/articles/introduction-to-convolutional-neural-networks/
[19]
Majid A, Khan MA, Yasmin M, Rehman A, Yousafzai A, Tariq U. Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection. Microsc Res Tech 2020; 83(5): 562-76.
[http://dx.doi.org/10.1002/jemt.23447] [PMID: 31984630]
[20]
Karkanis SA, Iakovidis DK, Maroulis DE, Karras DA, Tzivras M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans Inf Technol Biomed 2003; 7(3): 141-52.
[http://dx.doi.org/10.1109/TITB.2003.813794] [PMID: 14518727]
[21]
Khan MA, Sarfraz MS, Alhaisoni M, Albesher AA, Wang S, Ashraf I. StomachNet: Optimal deep learning features fusion for stomach abnormalities classification. IEEE Access 2020; 8: 197969-81.
[http://dx.doi.org/10.1109/ACCESS.2020.3034217]
[22]
Bernal J, Tajkbaksh N, Sanchez FJ, et al. Comparative validation of polyp detection methods in video colonoscopy: Results from the MICCAI 2015 endoscopic vision challenge. IEEE Trans Med Imaging 2017; 36(6): 1231-49.
[http://dx.doi.org/10.1109/TMI.2017.2664042] [PMID: 28182555]
[23]
Attique Khan M, Majid A, Hussain N, et al. Multiclass stomach diseases classification using deep learning features optimization. Comput Mater Continua 2021; 67(3): 3381-99.
[http://dx.doi.org/10.32604/cmc.2021.014983]
[24]
Godkhindi M, Gowda RM. Automated detection of polyps in CT colonography images using deep learning algorithms in colon cancer diagnosis. Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). Chennai, India. 01-02 Aug; 1722-8.
[http://dx.doi.org/10.1109/ICECDS.2017.8389744]
[25]
Mosleh Hmoud A-A, Ebrahim Mohammed S, Fawaz Waselallah A, et al. Deep learning algorithms for detection and classification of gastrointestinal diseases. Complexity 2021; 2021: 12.
[http://dx.doi.org/10.1155/2021/6170416]
[26]
Pozdeev A, Obukhova NA, Motyko AA. Automatic analysis of endoscopic images for polyps detection and segmentation. Proceedings of the 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). Saint Petersburg and Moscow, Russia. 2019; pp. 28-31 Jan; 1216-20.
[http://dx.doi.org/10.1109/EIConRus.2019.8657018]
[27]
Ahmed WS, Karim A. The impact of filter size and number of filters on classification accuracy in CNN. 2020 International Conference on Computer Science and Software Engineering. Duhok, Iraq. 2020.16-18 April;
[http://dx.doi.org/10.1109/CSASE48920.2020.9142089]
[28]
Omar RR, Han T, Al-Sumaidaee SAM, Chen T. Deep finger texture learning for verifying people. IET Biom 2019; 8(1): 40-8.
[http://dx.doi.org/10.1049/iet-bmt.2018.5066]
[29]
Khanday OM, Dadvandipour S. Convolutional neural networks and impact of filter sizes on image classification. Multidiszciplináris Tudományok 2020; 10(1): 55-60.
[http://dx.doi.org/10.35925/j.multi.2020.1.7]
[30]
Hossin M, Sulaiman M N. A review of evaluation metrics for data classification evaluations. Int J Data Mining Knowledge Manag Proc 2015; 5(2): 01-11.
[http://dx.doi.org/10.5121/ijdkp.2015.5201]
[31]
Fonollá C, Castillo-Olea B, Garcia-Zapirain , et al. Automatic colon polyp classification using convolutional neural network: A case study at Basque country. Proceedings of the 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Ajman, United Arab Emirates. 2019; pp. 10-12 Dec; 1-5.
[32]
Fonollá R, Van Der Sommen F, Schreuder RM, Schoon EJ, De With PH. Multi-modal classification of polyp malignancy using CNN features with balanced class augmentation. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy. 2019; pp. 74-8.
[http://dx.doi.org/10.1109/ISBI.2019.8759320]
[33]
Yue G, Wei P, Liu Y, Luo Y, Du J, Wang T. Automated endoscopic image classification via deep neural network with class imbalance Loss. IEEE Trans Inst Measure 2023; 72: 1-11.
[http://dx.doi.org/10.1109/TIM.2023.3264047]
[34]
Wang Wei, Yang Xin, Li Xin, Tang Jinhui. Convolutional-capsule network for gastrointestinal endoscopy image classification. Int J Intell Sys 2022; 37(9): 5796-815.
[http://dx.doi.org/10.1002/int.22815]
[35]
Yue G, Han W, Jiang B, Zhou T, Cong R, Wang T. Boundary constraint network with cross layer feature integration for polyp segmentation. IEEE J Biomed Health Inform 2022; 26(8): 4090-9.
[http://dx.doi.org/10.1109/JBHI.2022.3173948] [PMID: 35536816]
[36]
Anitha Mary X, Peniel Winifred Raj A, Suganthi Evangeline C, Mary Neebha T, Vinoth Babu Kumaravelu P. Multi-class classification of gastrointestinal diseases using deep learning techniques. Open Biomed J 2023; 17

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