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Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision
Amna Liaqat1, Muhammad Attique Khan2, Muhammad Sharif1
1Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan.
Abstract:
Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is time-consuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlighting the importance of each step have been presented. A detailed discussion and future directions have been provided at the end.
Insights
Early detection of gastric infections using automated Computer-Aided Diagnosis (CAD) with Wireless Capsule Endoscopy (WCE) is crucial. This review highlights WCE
Area of Science:
- Medical Imaging
- Gastroenterology
- Computer-Aided Diagnosis
Background:
- Gastric infections pose a significant health risk with high mortality rates.
- Manual diagnosis is time-consuming and costly, necessitating advanced solutions.
- Wireless Capsule Endoscopy (WCE) offers a less invasive diagnostic approach.
Purpose of the Study:
- To review the importance and scope of WCE in gastric infection detection.
- To outline the automated diagnostic process using WCE.
- To identify challenges and future directions in WCE-based gastric infection diagnosis.
Main Methods:
- Review of existing literature on WCE for gastric infections.
- Discussion of automated Computer-Aided Diagnosis (CAD) steps: preprocessing, feature extraction, segmentation, and classification.
- Analysis of challenges impacting accuracy and computation time.
Main Results:
- WCE is vital for medical imaging in detecting gastric issues.
- Automated CAD systems using WCE can aid endoscopy specialists.
- Challenges in WCE processing affect diagnostic efficiency.
Conclusions:
- Automated WCE systems are essential for timely and accurate gastric infection diagnosis.
- Addressing current challenges can improve detection rates and reduce diagnosis time.
- Further research is needed to optimize WCE-based diagnostic protocols.
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