Automated multi-model framework for malaria detection using deep learning and feature fusion

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. While traditional methods for diagnosis are effective, they face some limitations related to accuracy, time consumption, and manual effort. This study proposes an advanced, automated diagnostic framework for malaria detection using a multi-model architecture integrating deep learning and machine learning techniques. The framework employs a transfer learning approach that incorporates ResNet 50, VGG16, and DenseNet-201 for feature extraction. This is followed by feature fusion and dimensionality reduction via principal component analysis. A hybrid scheme that combines support vector machine and long short-term memory networks is used for classification. A majority voting mechanism aggregates outputs from all models to enhance prediction robustness. The approach was validated on a publicly available dataset comprising 27,558 microscopic thin blood smear images. The results demonstrated superior performance, achieving an accuracy of 96.47%, sensitivity of 96.03%, specificity of 96.90%, precision of 96.88%, and F1-score of 96.45% using the majority voting ensemble. Comparative analysis highlights the framework's advancements over existing methods in diagnostic reliability and computational efficiency. This work underscores the potential of AI-driven solutions in advancing malaria diagnostics and lays the foundation for applications in other blood-borne diseases.