Automated Wireless Capsule Endoscopy Image Classification using Reptile Search Optimization with Deep Learning Model
Automated Wireless Capsule Endoscopy Image Classification using Reptile Search Optimization with Deep Learning Model |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-4 |
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| Year of Publication : 2023 | ||
| Author : M. Amirthalingam, R. Ponnusamy |
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| DOI : 10.14445/22315381/IJETT-V71I4P224 | ||
How to Cite?
M. Amirthalingam, R. Ponnusamy, "Automated Wireless Capsule Endoscopy Image Classification using Reptile Search Optimization with Deep Learning Model, " International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 274-283, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P224
Abstract
Wireless Capsule Endoscopy (WCE) is one of the effective ways of investigating Gastrointestinal Tract (GI) diseases and implementing painless intestine imaging. Regardless, numerous concerns make its adaptation and extensive applicability challenges as tolerance, efficiency, performance, and safety. In addition, automatic investigation of the WCE information is more important for abnormality detection. Imaging a patient’s gastrointestinal tract through WCE generates large data that necessitates a special skill set from a medical practitioner and a substantial amount of time for analysis. Numerous vision and computer-aided -based solutions were introduced to overcome these challenges, yet, they do not offer the desired level of accuracy and further enhancement is still required. Therefore, this article presents an Automated Wireless Capsule Endoscopy Image Classification using Reptile Search Optimization with Deep Learning (WCEIC-RSADL) algorithm. The presented WCEIC-RSADL approach examines the WCE images using DL and hyperparameter tuning techniques. To achieve this, the presented WCEIC-RSADL technique involves bilateral filtering (BF) technique employed for the noise elimination process. Moreover, the presented WCEIC-RSADL technique enables the Inception v2 model for feature extraction purposes with RSA-based hyperparameter tuning purposes. Furthermore, the extreme learning machine (ELM) method can be exploited for WCE image classification. In order to exhibit the enhanced achievement of the WCEIC-RSADL approach, an extensive range of simulations were executed on the WCE image dataset. The results pointed out that the WCEIC-RSADL algorithm reaches promising performance over other approaches.
Keywords
Wireless capsule endoscopy, Deep learning, Image classification, Hyperparameter tuning, Reptile search algorithm.
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