ArticleDiagnostics (Basel, Switzerland)2020
Analyzing Malaria Disease Using Effective Deep Learning Approach.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ISTNet: a multi-scale transformer-based architecture for malaria cell classification.Medical & biological engineering & computing · 2026Article
- Malaria Parasite Cell Classification Using Transfer Learning with State-of-the-Art CNN Architectures.Biology · 2025Article
- Advances in Malaria Diagnostic Methods in Resource-Limited Settings: A Systematic Review.Tropical medicine and infectious disease · 2024Review
- An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.Parasites & vectors · 2024Article
- Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears.Open forum infectious diseases · 2023Article
- Reducing data dimension boosts neural network-based stage-specific malaria detection.Scientific reports · 2022Article
- Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.Sensors (Basel, Switzerland) · 2022Article
- Article
- Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis.IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.
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