Systematic Exploration Using Intelligent Computing Techniques for Clinical Diagnosis of Gastrointestinal Disorder: A Review
DOI:
https://doi.org/10.52756/ijerr.2023.v36.026Keywords:
Gastrointestinal disorders, intelligent computing, deep learning, machine learning, endoscopy, colonoscopyAbstract
In today's era, the growing ratio of Gastrointestinal (GI) diseases in human beings has become a crucial point of notice and must be diagnosed as early as possible. There are various methods to diagnose abdomen-related problems using medical imaging techniques like ultrasound, endoscopy, Colonoscopy, abdominal CT scan and digital X-ray, etc. Endoscopy is one of the most efficient medical imaging techniques for diagnosing gastrointestinal (GI) diseases. Manual diagnosis of endoscopic images may have a possibility of committing mistakes in properly detecting gastrointestinal disorders because tiny particles are involved in endoscopic images and may be responsible for critical disorders. However, manual diagnosis may ignore such information because of less efficiency of vision and observation. To avoid such problems, various models based on soft computing and neuro-fuzzy techniques have been proposed to detect and classify various gastrointestinal disorders. In this article, the authors propose a systematic review of previous research that has been carried out using intelligent computing methods. Here, various conventional approaches are discussed and compared. This review research shows performance limitations due to complex data models, heterogeneous datasets and the absence of intelligent feature selection methods in diagnosing gastrointestinal disorders.
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