Malaria Detection

Malaria detection research focuses on developing rapid, accurate, and accessible diagnostic tools, primarily leveraging deep learning models like convolutional neural networks (CNNs) and variations such as ResNet50, VGG19, InceptionV3, Xception, and specialized architectures like M2ANET, to analyze microscopic blood smear images. Current efforts concentrate on improving model generalization across diverse clinical settings and microscope types, often employing techniques like transfer learning, domain adaptation (e.g., contrastive domain adaptation), and data augmentation to address limitations in training data and variability in image quality. These advancements aim to improve diagnostic accuracy, reduce reliance on expert microscopists, and ultimately enhance malaria control and treatment, particularly in resource-limited regions.