Deep learning and machine learning for Malaria detection: overview,
challenges and future directions
- URL: http://arxiv.org/abs/2209.13292v1
- Date: Tue, 27 Sep 2022 10:33:00 GMT
- Title: Deep learning and machine learning for Malaria detection: overview,
challenges and future directions
- Authors: Imen Jdey, Ghazala Hcini and Hela Ltifi
- Abstract summary: This study uses a variety of machine learning and image processing approaches to detect and forecast the malarial illness.
In our research, we discovered the potential of deep learning techniques as smart tools with broader applicability for malaria detection.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To have the greatest impact, public health initiatives must be made using
evidence-based decision-making. Machine learning Algorithms are created to
gather, store, process, and analyse 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 has ended up curious about
it as of late. This study uses a variety of machine learning and image
processing approaches to detect and forecast the malarial illness. In our
research, we discovered the potential of deep learning techniques as smart
tools with broader applicability for malaria detection, which benefits
physicians by assisting in the diagnosis of the condition. We examine the
common confinements of deep learning for computer frameworks and organising,
counting need of preparing data, preparing overhead, realtime execution, and
explain ability, and uncover future inquire about bearings focusing on these
restrictions.
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