An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Abstract

Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

Keywords: K-mean; MRFO; clusters; features; malaria.

Figure 1

Proposed architecture for malaria classification.

Figure 2

Segmentation results (A) input image (B) luminance channel (C) a and b channel (D) cluster-1 (E) cluster-2 and (F) cluster-3.

Figure 3

Best features extraction and selection process for malaria cell classification.

Figure 4

Graphical representation of the MRFO method.

Figure 5

Mean predicted scores on the benchmark classifiers (A) ensemble (B) SVM. The ROC-AUC values are computed on benchmark classifiers as presented in Table 3.

Figure 6

Mean predicted scores using shuffle-net features vector (A) ensemble and (B) SVM.

Figure 7

Mean predicted scores using fused features vector (A) boosted, bagged, and discriminant kernels of ensemble classifier and (B) linear, cubic, and quadratic kernels of SVM.