İki Sıtma Veri Kümesinde Sıtma Hastalığı Tespitine Yönelik Yeni Bir Derin Öğrenme Yaklaşımı

Year 2023, , 254 - 272, 30.11.2023

İbrahim Çetiner , Halit Çetiner

https://doi.org/10.35193/bseufbd.1064187

Abstract

Sıtma, dişi sivrisineklerin ısırmasıyla insanlara bulaşan bulaşıcı bir ateşli hastalıktır. Bu hastalığın kısa sürede teşhis edilmesi önemlidir. Belirli bir probleme matematiksel olarak en iyi sayısal çözümü bulmak çoğu bölüm için en önemli konudur. Bu mantıkla geliştirilen derin öğrenme tabanlı sistemde, gerçek veriler ile modelin tahmin edilen sonucu arasındaki fark, kayıp fonksiyonları kullanılarak ölçülmektedir. Derin öğrenme modelinin eğitim sürecinde tahminlerdeki hata oranını en aza indirmek için modelde kullanılan ağırlık değerlerinin güncellenmesi gerekmektedir. Yapılan güncelleme işlemi, modelin tahmin sonucu üzerinde önemli bir etkiye sahiptir. Bu makale, sıtmayı kısa sürede teşhis etmeye yardımcı olacak yeni bir derin öğrenme tabanlı sıtma sınıflandırma yöntemi sunmaktadır. Bu amaçla, 21 katmanlı yeni bir Konvolüsyonel Sinir Ağı (CNN) modeli önerilmiştir. Önerilen bu model enfekte ve enfekte olmayan ince kırmızı kan hücresi görüntülerini sınıflandırmak için tasarlanmıştır. İnce kırmızı kan hücresi numunesi görüntüleri kullanılarak, Nadam ve RMSprop optimizasyon teknikleri ile %95 doğruluk elde edilmiştir. Elde edilen sonuçlar, önerilen yöntemin her bir optimizasyon algoritmasına göre etkinliğini göstermektedir.

Keywords

Sıtma, CNN, Optimizasyon Algoritmaları, Sınıflandırma

A Novel Deep Learning Approach to Malaria Disease Detection on Two Malaria Datasets

Abstract

Malaria is a contagious febrile disease transmitted to humans by the bite of female mosquitoes. It is important to diagnose this disease in a short period of time. Finding the mathematically best numerical solution to a particular problem is the most important issue for most departments. In deep learning-based systems developed, the difference between the real data and the predicted result of the model is measured using loss functions. To minimize the error rate in the predictions during the training process of deep learning models, the weight values used in the model should be updated. This update process has a significant effect on the model prediction result. This article presents a new deep learning-based malaria detection method that will help diagnose malaria in a short time. A new 21-layer Convolutional Neural Network (CNN) model is designed and proposed to describe infected and uninfected thin red blood cell images. By using thin red blood cell sample images, 95% accuracy was achieved with Nadam and RMSprop optimization techniques. The results obtained show the efficiency of the proposed method according to each optimization algorithm.

Keywords

Malaria, CNN, optimization algorithms, classification

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