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— Malaria remains a significant global health challenge, particularly in tropical and subtropical
regions. Traditional prediction methods, relying on historical data and basic statistical models, often lack the
precision needed to account for the complex dynamics of malaria transmission, influenced by environmental
and seasonal factors. To overcome these limitations, the EfficientNet architecture is proposed as a more
effective solution. EfficientNet, a family of convolutional neural networks, utilizes a compound scaling method
to uniformly expand network depth, width, and resolution, enabling it to capture intricate features without
excessive computational demands. By training on extensive datasets, EfficientNet can generalize across
diverse regions and conditions, enhancing its predictive accuracy. This improved precision supports timely
identification of potential outbreaks, aiding health authorities in resource allocation and intervention strategies.
The adoption of EfficientNet in malaria prediction represents a significant step forward, offering a robust tool
for more effective disease control and reducing the global impact of malaria.
Keywords:
Malaria Detection, EfficientNet-B0, Feature Extraction, Convolutional Neural Network (CNN), Image Classification, Transfer Learning, Data Augmentation, Confusion Matrix, Sensitivity (Recall), Precision, F1 Score, Medical Image Analysis, Deep Learning, Automated Diagnosis, Pretrained Model, Compound Scaling, Blood Smear Images.
Cite Article:
"EfficientNet-based deep learning model for accurate Malaria Prediction", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b52-b58, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504109.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator