Optimal Machine Learning Based Automated Malaria Parasite Detection and Classification Model Using Blood Smear Images

Malaria is a deadly disease which can be spread by the Plasmodium parasites. The existence of malaria can be identified by professional microscopists who examine the microscopic blood smear images. But it remains a challenge owing to the unavailability of experts, poor resolution images, and insufficient diagnostic quality. Therefore, image processing and machine learning (ML) models can be employed to detection of malaria parasites using blood smear images. With this motivation, this study introduces an optimal machine learning based automated malaria parasite detection and classification (OML-AMPDC) model using blood smear images. The proposed OML-AMPDC technique primarily undergoes pre-processing in two stages namely adaptive filtering (AF) based noise removal and contrast enhancement using CLAHE technique. Besides, the feature extraction process was implemented using Local Derivative Radial Patterns (LDRP). In addition, random forest (RF) classifier is applied to allot proper class labels to the blood smear images. Finally, particle swarm optimization (PSO) algorithm was utilized for optimally choose two parameters of the RF model, named maximum number of levels in every decision tree (max_depth) and number of trees in the forest (n_estimators). The design of PSO algorithm helps for enhancing the classification performance of the RF method. A wide-ranging experimental analysis is performed using benchmark dataset and the results reported the betterment of the OML-AMPDC technique over the recent approaches.

Automated summary

This study presents an optimal machine learning model for automated malaria parasite detection and classification using blood smear images. The model involves pre-processing stages, feature extraction using Local Derivative Radial Patterns, and classification using random forest. Additionally, the particle swarm optimization algorithm is utilized to optimize the parameters of the random forest model. Experimental results demonstrate the improved performance of the proposed model in malaria parasite detection.