Generative Augmentation and Temporal Feature Extraction With Deep Learning for Enhanced Malaria Stage Classification

Malaria is one of the most serious public health challenges that requires effective diagnostic methodologies in aid of its management and control. The work herein proposes a hybrid deep learning model that merges GANs with LSTMs to classify malaria parasites through imaging. Feature extraction is performed based on a VGG16 architecture with an integrated time‐distributed wrapper to improve the temporal understanding of the MP‐IDB dataset, comprising images of different stages in the lifecycle, ring, trophozoite, schizont, and gametocyte, of four different species: falciparum , malariae , ovale , and vivax . The results indeed unmask the critical improvement in the classification accuracy of the lifecycle stages of malaria parasites. This demonstrates the potential of deep learning approaches toward the development of malaria diagnostics. This work underlines the importance of robust AI‐driven methodologies in addressing public health concerns, thus opening the way for further research into automated disease detection systems.