A Review on Computational Methods Based on Deep Learning and Transfer Learning Techniques for Malaria Detection

Malaria continues to pose a significant health challenge worldwide, especially in most of under developing countries where most often there is limited healthcare resources. A death toll of nearly a million every year from two decades back is recorded by the World Health Organization. Doctors employ microscopic examination of patient's blood to identify the presence of malaria parasites in its initial phases, yet this method encounters issues related to both time consumption and accuracy. The need for precise, prompt, and effective detection of malaria parasites is crucial in the fight against this infectious disease. This review aims to highlight the recent most effective both preprocessing and classification methodologies used for detecting malaria parasites.