ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2022.018946 Article Malaria Blood Smear Classifcation Using Deep Learning and Best Features Selection Talha Imran 1 , Muhammad Attique Khan 2 , Muhammad Sharif 1 , Usman Tariq 3 , Yu-Dong Zhang 4 , Yunyoung Nam 5, * , Yunja Nam 5 and Byeong-Gwon Kang 5 1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan 2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan 3 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia 4 Department of Informatics, University of Leicester, Leicester, UK 5 Department of ICT Convergence, Soonchunhyang University, Asan, Korea * Corresponding Author: Yunyoung Nam. Email: ynam@sch.ac.kr Received: 27 March 2021; Accepted: 18 May 2021 Abstract: Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thou- sands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classifcation model is proposed, which takes images of both infected and healthy cells and preprocesses them in the L*a*b* color space by employing several contrast enhancement methods. Feature extraction is performed using two pretrained deep convolutional neu- ral networks, DarkNet-53 and DenseNet-201. The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm. Several classifers are effectuated on the reduced features, and the achieved results excel in both accuracy and time compared to previously proposed methods. Keywords: Malaria; preprocessing; deep learning; features optimization; classifcation 1 Introduction Malaria is a critical and intimidating disease, which has been of great concern for humans over a long period of time [1]. Malaria is the prime cause of thousands of deaths in both warm and cold regions worldwide, and it has reached over 228 million cases and 400 thousand deaths. Although it is a serious disease for both children and adults, children are the most likely affected, and almost 67% of children had fallen victim to it by 2019 [2]. The sporozoa bacillus of the genus Plasmodium is the root cause of malaria within the human body cells. It comprises six mainspring species, two of which, P. falciparum and P. vivax, are the most minacious. Apart from This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.