DALGACIK EVRİŞİMSEL SİNİR AĞI YÖNTEMİ İLE KORONAVİRÜS HASTALIĞININ TESPİTİ

Abidin Çalışkan

doi:10.17780/ksujes.1208283

EN TR

DETECTION OF CORONAVIRUS DISEASE USING WAVELET CONVOLUTIONAL NEURAL NETWORK METHOD

Abstract

Coronavirus (Covid-19) is a type of RNA-type virus that has been felt around the world since 2019 and has deadly consequences. The Covid-19 virus, usually shows its effectiveness in the lungs and causes various respiratory tract infections. In this study, a new artificial intelligence-based Convolutional Neural Network (CNN) model that can diagnose Covid-19 has been proposed. Spatial and spectral approaches are frequently used in image analysis and operations such as object identification. CNN models, on the other hand, generally process images in spatial areas and complete the training process by using the attributes obtained from there. In order to add a different perspective to the CNN model proposed in this study, the spatial and spectral processing of the input images was carried out. Thus, it was possible to extract different multi-resolution features. The missing parts of the multi-resolution analysis steps were completed using the so-called wavelet transform method. As a result, the overall accuracy of 98.48% was achieved in the experimental analyzes performed with the proposed approach, Wavelet CNN (W-CNN).

Keywords

DALGACIK EVRİŞİMSEL SİNİR AĞI YÖNTEMİ İLE KORONAVİRÜS HASTALIĞININ TESPİTİ

Öz

Koronavirüs (Kovid-19), 2019 yılından itibaren dünya genelinde hissedilen ve ölümcül sonuçları olan RNA tipi bir virüs türüdür. Kovid-19 virüsü, genellikle akciğerde etkinliğini göstermekte olup, çeşitli solunum yolu enfeksiyonlarına neden olmaktadır. Bu çalışmada, Kovid-19 tanısını gerçekleştirebilen yapay zekâ tabanlı yeni bir Evrişimsel Sinir Ağı (ESA) modeli önerilmiştir. Uzamsal ve spektral yaklaşımlar, görüntü analizlerinde ve nesne tanımlama gibi işlemlerde sıkça kullanılmaktadır. ESA modellerinde genellikle görüntüler uzamsal alanlarda işlenir ve eğitim sürecini buradan elde ettikleri öznitelikleri kullanarak tamamlarlar. Bu çalışmada önerilen ESA modeline farklı bir bakış açısı katabilmek için girdi görüntülerini mekânsal ve spektral olarak işlenmesi gerçekleştirildi. Böylece çok çözünürlüklü farklı özniteliklerin çıkartılması sağlandı. Çok çözünürlüklü analiz adımlarının eksik kısımlarını dalgacık dönüşümü denilen yöntem kullanılarak tamamlandı. Sonuç olarak, önerilen yaklaşım olan Dalgacık ESA (D-ESA) ile gerçekleştirilen deneysel analizlerde %98,48 genel doğruluk başarısı elde edilmiştir

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Abidin Çalışkan ^*
0000-0001-5039-6400
Türkiye

Yayımlanma Tarihi

15 Mart 2023

Gönderilme Tarihi

21 Kasım 2022

Kabul Tarihi

4 Mart 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 26 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1208283

IZ

https://izlik.org/JA68GX79BX

Kaynak Göster

RIS / Bibtex

APA

Çalışkan, A. (2023). DALGACIK EVRİŞİMSEL SİNİR AĞI YÖNTEMİ İLE KORONAVİRÜS HASTALIĞININ TESPİTİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 203-212. https://doi.org/10.17780/ksujes.1208283

Cited By

Development of an Effective Deep Learning Model for COVID-19 Detection from CT Images

Türk Doğa ve Fen Dergisi

https://doi.org/10.46810/tdfd.1472034