@article{10.11648/j.ajnna.20251101.12,
  author = {Emmanuel Osaze Oshoiribhor and Adetokunbo MacGregor John-Otumu},
  title = {AutoMalariaNet: A VGG16-Based Deep Learning Model for High-Performance Automated Malaria Parasite Detection in Blood Smear Images
},
  journal = {American Journal of Neural Networks and Applications},
  volume = {11},
  number = {1},
  pages = {11-27},
  doi = {10.11648/j.ajnna.20251101.12},
  url = {https://doi.org/10.11648/j.ajnna.20251101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20251101.12},
  abstract = {This research paper presents an automated malaria detection system using deep learning techniques to enhance diagnostic accuracy and efficiency, addressing the critical challenge of early and precise malaria diagnosis, especially in resource-constrained regions. Malaria remains a significant global health burden, particularly in tropical and subtropical regions where timely and accurate diagnosis is crucial for effective treatment and control. Traditional diagnostic methods, such as microscopic examination of blood smears, require skilled parasitologists and are often labor-intensive and time-consuming, making rapid detection difficult. To overcome these limitations, this study develops a deep learning-based malaria detection system integrating a Custom Convolutional Neural Network (CNN) and a pre-trained VGG16 model, trained on a publicly available malaria blood smear image dataset from Kaggle. Several data preprocessing techniques, including normalization and augmentation (rotation, flipping, scaling, and brightness adjustment), were applied to improve model generalization and robustness. The system is deployed through a web-based interface developed using Python, Flask, and HTML, allowing users to upload blood smear images and obtain real-time diagnostic results. Experimental evaluations demonstrate that the VGG16 model outperforms the Custom CNN, achieving an accuracy of 97%, precision of 96%, recall of 96.56%, and an F1-score of 97%, whereas the Custom CNN attained an accuracy of 87%, precision of 86%, recall of 85%, and an F1-score of 84.45%. These findings validate the effectiveness of deep learning in automating malaria detection and reducing reliance on manual microscopic examination, offering a scalable and accessible diagnostic tool for healthcare facilities with limited resources. Despite the success of the proposed system, further research is necessary to enhance model interpretability and trustworthiness. Future work should explore the integration of Vision Transformers (ViTs), Large Language Models (LLMs), and Ensemble Deep Learning techniques to improve malaria detection performance. Additionally, Explainable AI (XAI) methods, such as Grad-CAM, should be incorporated to provide visual explanations of model predictions, ensuring transparency and aiding medical professionals in understanding the decision-making process. By integrating these advancements, future systems can enhance both diagnostic accuracy and interpretability, making AI-driven malaria detection more reliable and widely applicable.
},
 year = {2025}
}

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TY  - JOUR
T1  - AutoMalariaNet: A VGG16-Based Deep Learning Model for High-Performance Automated Malaria Parasite Detection in Blood Smear Images

AU  - Emmanuel Osaze Oshoiribhor
AU  - Adetokunbo MacGregor John-Otumu
Y1  - 2025/04/17
PY  - 2025
N1  - https://doi.org/10.11648/j.ajnna.20251101.12
DO  - 10.11648/j.ajnna.20251101.12
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 11
EP  - 27
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20251101.12
AB  - This research paper presents an automated malaria detection system using deep learning techniques to enhance diagnostic accuracy and efficiency, addressing the critical challenge of early and precise malaria diagnosis, especially in resource-constrained regions. Malaria remains a significant global health burden, particularly in tropical and subtropical regions where timely and accurate diagnosis is crucial for effective treatment and control. Traditional diagnostic methods, such as microscopic examination of blood smears, require skilled parasitologists and are often labor-intensive and time-consuming, making rapid detection difficult. To overcome these limitations, this study develops a deep learning-based malaria detection system integrating a Custom Convolutional Neural Network (CNN) and a pre-trained VGG16 model, trained on a publicly available malaria blood smear image dataset from Kaggle. Several data preprocessing techniques, including normalization and augmentation (rotation, flipping, scaling, and brightness adjustment), were applied to improve model generalization and robustness. The system is deployed through a web-based interface developed using Python, Flask, and HTML, allowing users to upload blood smear images and obtain real-time diagnostic results. Experimental evaluations demonstrate that the VGG16 model outperforms the Custom CNN, achieving an accuracy of 97%, precision of 96%, recall of 96.56%, and an F1-score of 97%, whereas the Custom CNN attained an accuracy of 87%, precision of 86%, recall of 85%, and an F1-score of 84.45%. These findings validate the effectiveness of deep learning in automating malaria detection and reducing reliance on manual microscopic examination, offering a scalable and accessible diagnostic tool for healthcare facilities with limited resources. Despite the success of the proposed system, further research is necessary to enhance model interpretability and trustworthiness. Future work should explore the integration of Vision Transformers (ViTs), Large Language Models (LLMs), and Ensemble Deep Learning techniques to improve malaria detection performance. Additionally, Explainable AI (XAI) methods, such as Grad-CAM, should be incorporated to provide visual explanations of model predictions, ensuring transparency and aiding medical professionals in understanding the decision-making process. By integrating these advancements, future systems can enhance both diagnostic accuracy and interpretability, making AI-driven malaria detection more reliable and widely applicable.

VL  - 11
IS  - 1
ER  - 

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