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Classification of microscopic malaria parasitized images using deep learning feature fusion

Author(s)
Muhammad Asim Department of Computer Science, Khawaja Fareed University of Engineering and Technology, Rahim Yar Khan, Pakistan
Shahid Zaman Department of Computer Science, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan
Abdul Razzaq Department of Computer Science, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan
Salman Qadri Department of Computer Science, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan
Zahid Hussain NFC Institute of Engineering & Technology,Multan, Pakistan
Zaid Sarfraz Department of Computer Science, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan
Abstract
An infectious disease that causes a chronic and potentially life-threatening infection caused by microorganisms of the Plasmodium class is malaria or malarial disease. It is critical to detect the presence of Malaria parasites as early as possible to ensure that antimalarial treatment is adequate to cure the particular type of Plasmodium. This is to reduce death rates and to focus on various infections in the event of an adverse outcome. This study aimed to develop an artificial intelligence approach capable of separating parasitized erythrocytes from normal basophilic erythrocytes as well as platelets overlying the red blood cells to overcome the high cost of Malaria diagnostic equipment. The tone and texture characteristics of erythrocyte images were extracted using histogram thresholds and watershed methods and then fused with Squeeze Net and ShuffleNet algorithms. The measures included planning, preparing, approving, and testing Deep Convolution Neural Network Segmentation without preparation using a graphic processor unit. A total of 96% accuracy and specificity was obtained for the position of malaria in red blood cells based on the overall results. It has been demonstrated that deep learning techniques can be effective in clinical pathology. This provides new directions for development as well as increasing awareness of researchers in the medical field. The proposed methodology and application of fusion techniques on the images produce good results for the classification and digital identification of the disease.
Publication Details
Page(s) 38-46
DOI 10.54692/lgurjcsit.2023.0702473
Published Journal: Lahore Garrison University Research Journal of Computer Science and Information Technology, Volume: 7, Issue: 2, Year: 2023

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Keywords
Decision Making, malaria, Fusion Technique, Deep Feature, Digital Identification
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