Early Diagnosis of Malaria and its Classification Based on Automatic Counting of RBC

Abstract

Malaria is the leading cause of morbidity and mortality in tropical and subtropical countries. A conventional microscopic method used in the diagnosis of the disease has occasionally proved inefficient since it is time-consuming and results are difficult to reproduce. Alternative diagnosis techniques, which yield superior results, are quite expensive and hence inaccessible to developing countries where the disease is endemic. newlineIn this work, an accurate, rapid, and affordable model of malaria diagnosis using stained blood smear images was developed. The method makes use of the morphological, color, and texture features of Plasmodium parasites and erythrocytes. Images of infected and non-infected erythrocytes were acquired, and pre-processed, relevant features were extracted from them and eventually, the diagnosis was done based on the features extracted from the images. Diagnosis entailed detection of Plasmodium parasites, differentiation of different Plasmodium parasite stages and species, as well as parasitemia estimation. newlineImage pre-processing entailed reducing the size of the acquired images to speed up processing and median filtering to remove salt and pepper noise. Neural network classifiers are trained to detect and determine the life stages and species of Plasmodium parasites. newlineConvolutional Neural Network (CNN) classifiers trained with color, morphological, and texture features of infected stained blood smear images are suitable for the detection and classification of Plasmodium parasites into their respective stages and species. newlineA computer-aided automated diagnosis system can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are Red Blood Cell (RBC) counting and parasite classification. The counting process has been performed by different machine learning techniques, namely Hough transform, Zack s algorithm, Support Vector Machine (SVM) Weighted Similarity Extreme Learning Machine (WELM), AlexNet, and YOLO