Classification Of Covid Patients Based On Detection Of Lung X-Rays Using Local Binary Pattern Method

被引：0

作者：

Kartika, Dhian Satria Yudha ^{[1
]}

Wulansari, Anita ^{[1
]}

Safitri, Eristya Maya ^{[1
]}

Maulana, Hendra ^{[2
]}

Wibowo, Nur Cahyo ^{[1
]}

机构：

[1] Univ Pembangunan Nasl Vet Jawa Timur, Fac Comp Sci, Dept Informat Syst, Surabaya, Indonesia

[2] Univ Pembangunan Nasl UPN Vet Jawa Timur, Fac Comp Sci, Dept Informat, Surabaya, Indonesia

来源：

2021 4TH INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS (ISRITI 2021) | 2020年

关键词：

pandemic; covid-19; image processing; lung x-rays; local binary pattern; classification;

D O I：

10.1109/ISRITI54043.2021.9702828

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

High number of deaths due to Covid-19 outbreak affect people in various ways including their economic and psychological side. Previous studies were carried out in analyzing various symptoms in COVID-19 patients. Patients in severe conditions are usually found with a white spot in their lungs. Therefore chest x-ray is one of the necessary medical assessment to examine the patients. This study focus on determining whether a patient suffered from COVID-19 by analyzing their chest X-rays photos. A total of 864 X-rays photos were used as a dataset. Earlier steps in processing the dataset included removing the noise, equalizing the size and increasing the accuracy value. The Local Binary Pattern (LBP) method was used to extract the dataset feature. The performance analysis result was a precision value of 78.5%, recall of 78%, andcmeasure of 79%.

引用

页数：5

共 50 条

[41] Analysis of local binary pattern for emphysema classification in lung CT image [J].

Tuba, Eva ;

Strumberger, Ivana ;

Bacanin, Nebojsa ;

Tuba, Milan .

PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI-2019), 2019,

[42] Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? [J].

Tuan D. Pham .

Health Information Science and Systems, 9

[43] Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? [J].

Pham, Tuan D. .

HEALTH INFORMATION SCIENCE AND SYSTEMS, 2021, 9 (01)

[44] Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays [J].

Subramanian, Vaishnavi ;

Wang, Hongzhi ;

Wu, Joy T. ;

Wong, Ken C. L. ;

Sharma, Arjun ;

Syeda-Mahmood, Tanveer .

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT VI, 2019, 11769 :522-530

[45] Detection of electrocardiographic changes in partial epileptic patients using local binary pattern based composite feature [J].

T. Sunil Kumar ;

Vivek Kanhangad .

Australasian Physical & Engineering Sciences in Medicine, 2018, 41 :209-216

[46] Covid-19 Detection From Chest X-Rays Using Modified VGG 16 Model [J].

Karajah, Eman Naser ;

Awad, Mohammad .

2021 INTERNATIONAL CONFERENCE ON PROMISING ELECTRONIC TECHNOLOGIES (ICPET 2021), 2021, :46-51

[47] CoviNet: Automated COVID-19 Detection from X-rays using Deep Learning Techniques [J].

Lafraxo, Samira ;

El Ansari, Mohamed .

2020 6TH IEEE CONGRESS ON INFORMATION SCIENCE AND TECHNOLOGY (IEEE CIST'20), 2020, :489-494

[48] COVID-19 detection using hybrid deep learning model in chest x-rays images [J].

Mahajan, Shubham ;

Raina, Akshay ;

Gao, Xiao-Zhi ;

Pandit, Amit Kant .

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2022, 34 (05)

[49] A Survey of COVID-19 Detection From Chest X-Rays Using Deep Learning Methods [J].

Dornadula, Bhargavinath ;

Geetha, S. ;

Anbarasi, L. Jani ;

Kadry, Seifedine .

INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING, 2022, 18 (01)

[50] Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms [J].

AbdElhamid, Abeer A. ;

AbdElhalim, Eman ;

Mohamed, Mohamed A. ;

Khalifa, Fahmi .

APPLIED SCIENCES-BASEL, 2022, 12 (04)

← 1 2 3 4 5 →