deep learning for identification malaria diseases from microscopic image

Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. in this study, we propose a novel approach for identifying malaria parasites in microscopic images using the googlenet. our method includes enhancement with the agcs method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and googlenet-based classification. we evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of googlenet for automated malaria diagnosis.