TY  - JOUR
T1  - FPGA implementation of deep learning model utilizing different normalization algorithms for COVID-19 diagnosis
TT  - COVID-19 teşhisi için farklı normalizasyon algoritmaları kullanılan derin öğrenme modelinin FPGA gerçeklemesi
AU  - Karakaya, Barış
AU  - Zirekgür, Merve
PY  - 2024
DA  - July
Y2  - 2024
DO  - 10.28948/ngumuh.1427827
JF  - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
JO  - NÖHÜ Müh. Bilim. Derg.
PB  - Niğde Ömer Halisdemir Üniversitesi
WT  - DergiPark
SN  - 2564-6605
SP  - 905
EP  - 916
VL  - 13
IS  - 3
LA  - en
AB  - Normalization is utilized to remove outliers from the dataset and address network bias. In this research, Mean-Variance-Softmax-Rescale (MVSR) and Min-Max normalizations are employed in various combinations for the diagnosis of COVID-19 using a Convolutional Neural Network (CNN)-based Deep Learning (DL) model, aimed at enhancing network accuracy. To accomplish this, the CNN model is developed within the Google Colab environment and trained using a publicly available dataset consisting of chest X-ray images related to COVID-19. The dataset is normalized using different combinations of the MVSR and Min-Max normalization algorithms to compare model accuracy. Each normalized dataset is used for model training, and subsequently, each trained model has been saved as a .h5 file and loaded into the Kria KV260 Vision AI Starter Kit FPGA for the testing phase. The most accurate results are obtained when MVSR and Min-Max normalizations are applied simultaneously. This high-performing scenario is re-evaluated with COVID-19 and normal X-ray images on FPGA configuration. Experimentally, the highest accuracy is achieved in real-time with the MVSR+Min-Max scenario, reaching 93%. The model's precision, recall, and F1-Score values are determined as 0.91, 0.96, and 0.93, respectively.
KW  - Artificial Intelligence
KW  - Deep Learning
KW  - Image Processing
KW  - Normalization
N2  - Normalizasyon, veri setindeki aykırı değerleri ortadan kaldırmak ve ağ yanlılığını gidermek için kullanılmaktadır. Bu çalışmada, COVID-19 hastalığının teşhisi için kullanılan Konvolüsyonel Sinir Ağları (CNN) tabanlı Derin Öğrenme (DL) modeli ile farklı kombinasyonlarda Mean-Variance-Softmax-Rescale (MVSR) ve Min-Max normalizasyonları kullanılarak ağın doğruluğunun artırılması amaçlanmıştır. Bu amaçla, CNN modeli Google Colab ortamında oluşturulmuş ve COVID-19 için göğüs X-ray görüntülerini içeren açık bir veri seti ile eğitilmiştir. Veri seti, model doğruluğunu karşılaştırmak için MVSR ve Min-Max normalizasyon algoritmalarının farklı kombinasyonlarıyla normalize edilmiştir. Her eğitilmiş model, bir .h5 dosyası olarak kaydedilmiş ve ardından test aşaması için Kria KV260 Vision AI Starter Kit FPGA kartına yüklenmiştir. En yüksek doğruluk sonuçları, MVSR ve Min-Max normalizasyonlarının birlikte uygulandığı senaryo ile elde edilmiştir. En iyi performansı veren senaryo, COVID-19 ve normal X-ray görüntüleri ile FPGA yapılandırmasında tekrar test edilmiştir. En yüksek doğruluk, MVSR+Min-Max senaryosuyla deneysel olarak gerçekleştirilmiş ve %93 olarak elde edilmiştir. Modelin kesinliği, duyarlılığı ve F1-Skor değerleri sırasıyla 0.91, 0.96 ve 0.93 olarak belirlenmiştir.
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UR  - https://doi.org/10.28948/ngumuh.1427827
L1  - https://dergipark.org.tr/tr/download/article-file/3692043
ER  -