ArticleDiagnostics (Basel, Switzerland)2022
Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 34 citations in OpenAlex.
- Intelligent automation cognitive system for accurate malaria diagnosis using digital blood smears.PloS one · 2026Article
- Leveraging MobileNetV2 and deep learning innovation for high accuracy Plasmodium Vivax detection in blood smears.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Article
- Automated multi-model framework for malaria detection using deep learning and feature fusion.Scientific reports · 2025Article
- Advancing Prostate Cancer Diagnostics: A ConvNeXt Approach to Multi-Class Classification in Underrepresented Populations.Bioengineering (Basel, Switzerland) · 2025Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Article
- Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest.Diagnostics (Basel, Switzerland) · 2024Article
- COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images.Journal of imaging · 2023Article
- Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework.Diagnostics (Basel, Switzerland) · 2023Article
- An Alternative Diagnostic Method forDiagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.
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