Computer aided medical image analysis for capsule endoscopy using conventional machine learning and deep learning

被引：0

作者：

Jani, Kuntesh ^{[1
]}

Srivastava, Rajeev ^{[1
]}

Srivastava, Subodh ^{[2
]}

Anand, Animesh ^{[1
]}

机构：

[1] BHU, Comp Sci & Engn Dept, Indian Inst Technol, Varanasi, Uttar Pradesh, India

[2] Natl Inst Technol, Elect & Commun Engn Dept, Patna, Bihar, India

来源：

2019 7TH INTERNATIONAL CONFERENCE ON SMART COMPUTING & COMMUNICATIONS (ICSCC) | 2019年

关键词：

CAD; capsule endoscopy; medical image analysis; deep learning;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Large population of the world suffers from diseases related to gastrointestinal (GI) tract. The most modern technique available to scan the GI tract is capsule endoscopy (CE). It is a non-sedative, non-invasive and patient-friendly alternative to conventional endoscopy. However, CE generates approximately 55000 to 60000 images which make the diagnosis process time consuming and tiresome for physicians. Also the diagnosis varies from expert to expert. Hence a computer-aided diagnosis system is a must. This study, addresses a multi-class medical image analysis problem using image processing and machine learning techniques. It presents a computer aided diagnosis (CAD) system based on conventional machine learning as well as deep learning for automatic abnormality detection in GI tract. The system performs with an accuracy of 95.11% and precision of 93.9%.

引用

页码：290 / 294

页数：5

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