A Novel Hybrid Deep Learning Framework for Automated Malaria Parasite Detection in Microscopic Blood Smear Images

Abstract

Malaria remains one of the most pressing global health challenges, particularly in endemic regions where a timely and accurate diagnosis is crucial. In this work, we propose a hybrid deep learning framework that combines advanced image pre-processing, a custom convolutional neural network (CNN) for feature extraction, an enhanced feature selection mechanism based on Levy-flight Grey Wolf Optimization (GWO), and a support vector machine (SVM) classifier. The proposed methodology is designed to mitigate the limitations of manual microscopy and conventional computer-aided diagnosis, achieving superior detection accuracy while reducing computational overhead. Experimental evaluation on multiple publicly available malaria datasets demonstrates an accuracy exceeding 97%, outperforming several baseline deep learning architectures. We discuss the strengths, challenges, and future potential of integrating domain-specific pre-processing with modern optimization techniques in medical image analysis.