A Comprehensive Analysis of Deep Learning-Based Approaches for the Prediction of Gastrointestinal Diseases Using Multi-class Endoscopy Images

Bhardwaj, Priya; Kumar, Sanjeev; Kumar, Yogesh

doi:10.1007/s11831-023-09951-8

Cited by 9 publications

(1 citation statement)

References 37 publications

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“…The detection of GIT disease using colonoscopy is a current area of research, and deep learning has consistently been crucial in its diagnosis. 24,25 In conclusion, the classification of gastrointestinal diseases is an important aspect of medical research, and several research papers have been published in this area. These papers describe the classification of various gastrointestinal diseases based on their clinical, histological, and molecular characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

CG‐Net: A novel CNN framework for gastrointestinal tract diseases classification

Siddiqui,

Akram,

Ashraf

et al. 2024

Int J Imaging Syst Tech

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The classification of medical images has had a significant influence on the diagnostic techniques and therapeutic interventions. Conventional disease diagnosis procedures require a substantial amount of time and effort to accurately diagnose. Based on global statistics, gastrointestinal cancer has been recognized as a major contributor to cancer‐related deaths. The complexities involved in resolving gastrointestinal tract (GIT) ailments arise from the need for elaborate methods to precisely identify the exact location of the problem. Therefore, doctors frequently use wireless capsule endoscopy to diagnose and treat GIT problems. This research aims to develop a robust framework using deep learning techniques to effectively classify GIT diseases for therapeutic purposes. A CNN based framework, in conjunction with the feature selection method, has been proposed to improve the classification rate. The proposed framework has been evaluated using various performance measures, including accuracy, recall, precision, F1 measure, mean absolute error, and mean squared error.

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Section: Literature Reviewmentioning

confidence: 99%