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Scopus 🔓 Açık Erişim YÖKSİS DOI Eşleşti SJR Q2

Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images

International Journal of Imaging Systems and Technology · Temmuz 2023

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YÖKSİS Kayıtları
Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY · 2023 SCI-Expanded
Doç. Dr. KENAN ERDEM →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 4 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Adrenal tumor characterization on magnetic resonance images.
2019 ISSN: 0899-9457 SCI-Expanded
Doç. Dr. HAKAN CEBECİ →
Adrenal tumor characterization on magnetic resonance images
2020 ISSN: 0899-9457 SCI-Expanded
Prof. Dr. MUSTAFA KOPLAY →
Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images
2023 ISSN: 0899-9457 SCI-Expanded Q2
Doç. Dr. KENAN ERDEM →
MResCaps: Enhancing capsule networks with parallel lanes and residual blocks for high‐performance medical image classification
2024 ISSN: 0899-9457 SCI Q2
Doç. Dr. İLKER ALİ ÖZKAN →

Makale Bilgileri

Dergi International Journal of Imaging Systems and Technology
ISSN08999457
Yayın TarihiTemmuz 2023
Cilt / Sayfa33 · 1144-1159
DOI10.1002/ima.22914
Scopus ID2-s2.0-85161089102
Erişim🔓 Açık Erişim
Özet COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.

Yazarlar (15)

1
Kenan Erdem
2
Mehmet Ali Kobat
ORCID: 0000-0002-2217-2925
3
Mehmet Nail Bilen
ORCID: 0000-0003-1468-2930
4
Yunus Balik
ORCID: 0009-0007-4109-1315
5
Sevim Alkan
ORCID: 0009-0000-2147-7375
6
Feyzanur Cavlak
ORCID: 0009-0008-9279-3816
7
Ahmet Kursad Poyraz
ORCID: 0000-0001-8992-1743
8
Prabal Datta Barua
ORCID: 0000-0001-5117-8333
9
Ilknur Tuncer
ORCID: 0000-0003-1549-7008
10
Sengul Dogan
ORCID: 0000-0001-9677-5684
11
Mehmet Baygin
12
Mehmet Erten
ORCID: 0000-0002-6664-4568
13
Turker Tuncer
ORCID: 0000-0002-1425-4664
14
Ru San Tan
ORCID: 0000-0003-2086-6517
15
U. Rajendra Acharya
ORCID: 0000-0003-2689-8552

Anahtar Kelimeler

AlexNet biomedical image classification CT image classification Hybrid-Patch-Alex transfer learning

Kurumlar

Basaksehir Cam and Sakura City Hospital
Istanbul Türkiye
Duke-NUS Medical School
Singapore City Singapore
Elazig Fethi Sekin City Hospital
Elazig Turkey
Elazig Governorship
Elazig Turkey
Erzurum Teknik Üniversitesi
Erzurum Turkey
Firat Üniversitesi
Elazig Turkey
Firat Üniversitesi Tip Fakültesi
Elazig Turkey
National Heart Centre Singapore
Singapore City Singapore
Selçuk Üniversitesi
Selçuklu Turkey
University of Southern Queensland
Toowoomba Australia
University of Technology Sydney
Sydney Australia
Scimago Dergi (ISSN Eşleşmesi)
International Journal of Imaging Systems and Technology
Q2
SJR Skoru0,601
H-Index67
YayıncıJohn Wiley and Sons Inc
ÜlkeUnited States
Biomedical Engineering (Q2)
Computer Science Applications (Q2)
Computer Vision and Pattern Recognition (Q2)
Electrical and Electronic Engineering (Q2)
Electronic, Optical and Magnetic Materials (Q2)
Radiology, Nuclear Medicine and Imaging (Q2)
Software (Q2)
Health Informatics (Q3)
Dergi sayfasına git

Metrikler

10
Atıf
15
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

KE
KENAN ERDEM
Doç. Dr.

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