Deep Learning and Machine Learning for Malaria Detection: Overview, Challenges and Future Directions

Imen Jdey, Ghazala Hcini and Hela Ltifi
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Imen Jdey: Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia2ReGIM-Laboratory Research Groups in Intelligent Machines (LR11ES48), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia
Ghazala Hcini: Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia2ReGIM-Laboratory Research Groups in Intelligent Machines (LR11ES48), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia
Hela Ltifi: Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia2ReGIM-Laboratory Research Groups in Intelligent Machines (LR11ES48), National Engineering School of Sfax (ENIS), University of Sfax, Tunisia

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 05, 1745-1776

Abstract: Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints.

Keywords: Malaria diagnosis; machine learning; deep learning; convolutional neural network; hybrid algorithms (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023300045

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