Improvement in Classification Accuracy Rate Using Multiple Classifier Fusion Towards Computer Vision Detection of Malaria Parasite (Plasmodium vivax)

Abstract

Background: The main method used for the laboratory confirmation of malaria is the conventional light microscopy; however, microscopy has three main disadvantages: I) it is time-consuming and labor-intensive; II) its results depend heavily on good techniques, reagents and microscopes; III) in many cases decisions about treatment are often taken without using the result of microscopy because of long delays in providing the results to the clinician. Hence, an extreme necessity of the fast automatic detection of the disease is required to diagnose and treat promptly. Objectives: Through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. Materials and Methods: This study was conducted using 400 confirmed images of blood slides infected with malaria parasite. The MATLAB software was used for the implementation of computation procedures. Using five extracted features (flat texture, saturation channel histogram, color histogram, gradient, and granulometry) and six classifiers (k-Nearest Neighbors (k-NN), 1-Nearest Neighbor (1-NN), decision tree (DT), Fisher, linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA)), images were classified into two classes: parasitic and nonparasitic. Then, classifier fusion was done using several algorithms: mean, min, max, stack, median, Adaboost, and bagging. Results: Using six classifiers separately, the highest accuracy was obtained 92% using the k-NN classifier. The highest accuracy of the classifiers' fusion was obtained using the Adaboost algorithm with 95.5% success rate. Conclusions: By comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy.

Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması

“…Sıtma hastalığının sınıflandırılmasında ve tespitinde literatürde birçok yöntem kullanılmıştır. Bunlar; Destek Vektör Makine (DVM) [6], k-En Yakın Komşuluk algoritması (k-Ek) [7], Yapay Sinir Ağları (YSA) [8], AdaBoost algoritması [9], Otsu metodu [2], Çok Katmanlı Algılayıcı (ÇKA) [10], İstatistiksel olarak geliştirilmiş kural tabanlı sınıflandırma metodu [11], Radyal tabanlı fonksiyon sınıflandırıcı [12], İnce ayarlı Evrişimsel Sinir Ağları (ESA) modeli [13] ve literatürde birçok alanda kullanılan Derin öğrenme algoritmaları [5,14]. Aynı zamanda son yıllarda, literatürde popüler hale gelen derin öğrenme yöntemi, sıtma hastalığının sınıflandırılmasında olduğu gibi, birçok alanda kullanılmaktadır [15,16].…”