By Dr. Chinta SidharthanJun 12 2024Reviewed by Lily Ramsey, LLM In a recent study published in Scientific Reports, a team of researchers proposed using an artificial intelligence tool that uses deep learning to examine red blood cell images in blood smears for the timely detection of malaria.
Given that regions in Africa, South East Asia, and the Mediterranean experience over 70% of malaria cases, the process of detecting malaria through blood smears becomes very laborious and significantly increases the pathologist’s workload. About the study In the present study, the researchers proposed a deep-learning-based AI tool to detect malaria from images of red blood cells accurately. They also compared the proposed EfficientNet-B2 model against other deep-learning models and used ten-fold cross-validation for efficacy validation.
The deep-learning model EfficientNet-B2 used in this study was a Convolutional Neural Networks model, which has been widely employed for problems involving image classification. The study also compared the performance of numerous pre-trained models such as CNN, Visual Geometry Group , Inception, DenseNet121, MobileNet, and ResNet, compared to the deep-learning model proposed in this study.