ArticleElectronics2022
ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification.
Article in Electronics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 55 citations in OpenAlex.
- The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm.Technology in cancer research & treatmentTrial
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Image Processing and Deep Convolutional Neural Network Method for Automated Malaria Parasite Detection in Thin Blood Slide Images.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Performance validation of deep-learning-based approach in stool examination.Parasites & vectors · 2025Article
- Advanced deep learning for multi-class colorectal cancer histopathology: integrating transfer learning and ensemble methods.Quantitative imaging in medicine and surgery · 2025Article
- Improving Malaria diagnosis through interpretable customized CNNs architectures.Scientific reports · 2025Article
- Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks.Journal of imaging · 2024Article
- Article
- ReRNet: A Deep Learning Network for Classifying Blood Cells.Technology in cancer research & treatmentArticle
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: (1)People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. Methods: (2)In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pretrained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. Results: (3)We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. Conclusions: (4)The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods.
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