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

共 50 条

[21] A Machine Learning Application for Medical Image Analysis Using Deep Convolutional Neural Networks (CNNs) and Transfer Learning Models for Pneumonia Detection [J].

Shirwaikar, Rudresh ;

Anitha, V. ;

Rao, Vuda Sreenivasa ;

Kaushal, Ashish Kumar ;

Kakad, Shital ;

Khan, Mohammad Ahmar .

JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (05) :2316-2324

[22] Deep learning on medical image analysis [J].

Wang, Jiaji ;

Wang, Shuihua ;

Zhang, Yudong .

CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2025, 10 (01) :1-35

[23] Deep Learning in Medical Image Analysis [J].

Chan, Heang-Ping ;

Samala, Ravi K. ;

Hadjiiski, Lubomir M. ;

Zhou, Chuan .

DEEP LEARNING IN MEDICAL IMAGE ANALYSIS: CHALLENGES AND APPLICATIONS, 2020, 1213 :3-21

[24] Survey on machine learning applied to medical image analysis [J].

Olivier, Aurelien ;

Hoffmann, Clement ;

Mansour, Ali ;

Bressollette, Luc ;

Clement, Benoit .

2021 14TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2021), 2021,

[25] Artificial intelligence and deep learning for small bowel capsule endoscopy [J].

Trasolini, Roberto ;

Byrne, Michael F. .

DIGESTIVE ENDOSCOPY, 2021, 33 (02) :290-297

[26] A configurable deep learning framework for medical image analysis [J].

Chen, Jianguo ;

Yang, Nan ;

Zhou, Mimi ;

Zhang, Zhaolei ;

Yang, Xulei .

NEURAL COMPUTING & APPLICATIONS, 2022, 34 (10) :7375-7392

[27] Trends in Deep Learning for Medical Hyperspectral Image Analysis [J].

Khan, Uzair ;

Paheding, Sidike ;

Elkin, Colin P. ;

Devabhaktuni, Vijaya Kumar .

IEEE ACCESS, 2021, 9 (09) :79534-79548

[28] A configurable deep learning framework for medical image analysis [J].

Jianguo Chen ;

Nan Yang ;

Mimi Zhou ;

Zhaolei Zhang ;

Xulei Yang .

Neural Computing and Applications, 2022, 34 :7375-7392

[29] Explainable Deep Learning in Spectral and Medical Image Analysis [J].

Liu, Xuyang ;

Duan, Chaoshu ;

Cai, Wensheng ;

Shao, Xueguang .

PROGRESS IN CHEMISTRY, 2022, 34 (12) :2561-2572

[30] Deep Learning and Big DataTechnologies in Medical Image Analysis [J].

Rastogi, Priyanka ;

Singh, Vijendra ;

Yadav, Monika .

2018 FIFTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (IEEE PDGC), 2018, :60-63

← 1 2 3 4 5 →