Evrişimli Sinir Ağı (ESA) Mimarileri ile Hücre Görüntülerinden Sıtmanın Tespit Edilmesi

Öz

Sıtma, dünyanın birçok bölgesinde yaygın olarak görülen enfekte sivrisineklerin ısırıkları yoluyla insanlara bulaşan parazitlerin neden olduğu hayatı tehdit eden bir hastalıktır. Plasmodium adlı kan paraziti bu hastalığına sebep olmaktadır. Sıtmanın erken teşhisi ve tedavisi, özellikle hastalığın yaygın olduğu gelişmekte olan ülkelerde, hastalık ve ölüm oranlarının azaltılması açısından çok önemlidir. Sıtma teşhisinde kullanılan klasik yöntem, uzmanlar tarafından kırmızı kan hücrelerinin mikroskop yardımıyla incelenmesiyle tespitidir. Bu yöntem, sadece uzmanın bilgi ve deneyimine dayandığı için verimsizdir. Günümüzde hastalığın yüksek oranda doğru bir şekilde tespiti için makine öğrenmesi yöntemleri kullanılmaktadır. Bu çalışmada, hücreyi parazitli veya parazitsiz olarak tespit için Evrişimli Sinir Ağı (ESA) mimarisi önerilmiştir. Önerilen ESA mimarisine ek olarak VGG-19, InceptionResNetV2, DenseNet121 ve EfficientNetB3 gibi önceden eğitilmiş ESA mimarilerinin performansları ile önerdiğimiz modelin performansı karşılaştırılmıştır. Önerdiğimiz ESA mimarisinde National Institute of Health (NIH) tarafından yayınlanan Sıtma Veri Kümesi kullanılarak deneyler gerçekleştirilmiştir. Mimarimiz %98,9 doğruluk ile çalışmaktadır. Çalışmanın sonuçları, Plasmodium içeren hücre görüntülerinin doğruluğunu artırmada etkili olduğunu göstermektedir.

Anahtar Kelimeler

• Sıtma
• ESA
• Önceden eğitilmiş model
• Hastalık tespiti

Detection of Malaria with Convolutional Neural Network (CNN) Architectures Using Cell Images

Öz

This article highlights that malaria is widespread worldwide through infected mosquitoes transmitted to humans and is caused by the blood parasite Plasmodium. Early diagnosis and treatment of malaria plays an important role in reducing morbidity and mortality, especially in developing countries. The traditional method of diagnosing malaria involves examining red blood cells under a microscope, but this method can be inefficient as it relies on expert knowledge. A highlight of the paper is the use of machine learning methods in malaria diagnosis. In particular, a Convolutional Neural Network (CNN) architecture is proposed to detect parasitized and non-parasitized cells. Furthermore, the performance of this proposed model is compared with pre-trained CNN architectures such as VGG-19, InceptionResNetV2, DenseNet121 and EfficientNetB3. In the experiments, the Malaria Dataset published by the National Institute of Health (NIH) was used and the proposed CNN architecture achieved 98.9% accuracy. These results show that the proposed model is effective in accurately recognizing cell images containing Plasmodium. This study highlights the potential of AI-based methods in the field of malaria diagnosis.

Anahtar Kelimeler

• Malaria
• CNN
• Pre-trained model
• Disease detection

Kaynakça

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