TY  - JOUR
T1  - A Novel Multi-Head Attention Framework for COVID-19 Detection: Hybrid Integration of MobileNet and VGG19 with Enhanced Feature Learning
TT  - COVID-19 Tespiti için Yeni Bir Çok Başlı Dikkat Çerçevesi: MobileNet ve VGG19 Tabanlı Geliştirilmiş Özellik Öğrenimi ile Hibrit Entegrasyon
AU  - Kılıç, Şafak
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.21605/cukurovaumfd.1653486
JF  - Çukurova Üniversitesi Mühendislik Fakültesi Dergisi
PB  - Çukurova Üniversitesi
WT  - DergiPark
SN  - 2757-9255
SP  - 655
EP  - 670
VL  - 40
IS  - 3
LA  - en
AB  - The COVID-19 pandemic has underscored the urgent need for rapid, accurate, and affordable diagnostic tools to complement RT-PCR testing. This study proposes a novel multi-head attention framework that integrates VGG19 and MobileNet for automated COVID-19 detection from chest X-rays. The model employs a hybrid mechanism combining spatial, channel, and self-attention components, enhancing feature representation while preserving efficiency.Evaluations on 7,132 chest X-ray images across four categories (COVID-19, Normal, Pneumonia, Tuberculosis) demonstrated outstanding performance: 99.0% accuracy, 99.0% macro and weighted F1-scores, with near-perfect class-specific results (100% Tuberculosis, 99.7% COVID-19, 99.5% Normal, 96.0% Pneumonia). Inference time was only 63 ms per image, with a compact 14.8 MB model size.These results surpass baseline MobileNet and DenseNet121 by 2.63% and 4.32%, respectively. The proposed framework offers reliable rapid screening and differential diagnosis, supported by interpretable attention maps, making it highly suitable for deployment in resource-limited healthcare and point-of-care settings.
KW  - COVID-19 Detection
KW  - Deep Learning
KW  - Multi-Head Attention Mechanism
KW  - Medical Image Analysis
KW  - VGG19
N2  - COVID-19 pandemisi, RT-PCR testlerini destekleyecek hızlı, doğru ve maliyet etkin tanı araçlarına duyulan ihtiyacı ortaya koymuştur. Bu çalışmada, göğüs röntgeni görüntülerinden otomatik COVID-19 tespiti için VGG19 ve MobileNet mimarilerini entegre eden yeni bir çok başlı dikkat çerçevesi önerilmektedir. Model, uzamsal, kanal ve çok başlı öz-dikkat mekanizmalarını birleştirerek özellik çıkarımını güçlendirirken hesaplama verimliliğini korumaktadır.Yaklaşımımız 7.132 görüntüden oluşan dört sınıflı veri kümesinde test edilmiştir (COVID-19, Normal, Pnömoni, Tüberküloz). Dikkat mekanizmasıyla geliştirilmiş MobileNet %99,0 doğruluk, makro ve ağırlıklı F1 skorları elde etmiştir. Sınıf bazında %100 Tüberküloz, %99,7 COVID-19, %99,5 Normal ve %96,0 Pnömoni doğruluğu kaydedilmiştir. Ayrıca model, 14,8 MB boyutu ve 63 ms çıkarım süresi ile klinik uygulanabilirliğe sahiptir.Sonuçlar, mevcut yöntemlere göre %2,63–%4,32 iyileşme göstermekte olup, modelin güvenilir hızlı tarama ve ayırıcı tanıda etkili olduğunu göstermektedir.
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UR  - https://doi.org/10.21605/cukurovaumfd.1653486
L1  - https://dergipark.org.tr/tr/download/article-file/4671178
ER  -