An Efficient Red Blood Cell Smear-based Malaria Disease Detection using Digital Image Processing

Architected an automated malaria detection pipeline on RBC smear images using EfficientNetB3, InceptionV3, and VGG16; optimized diagnostic precision through transfer learning and weighted aggregating and deployed it on the web using Flask Framework.

Attained model performances with EfficientNet B3 (98.76% accuracy), VGG16 (94.45% accuracy), and InceptionV3 (85.34% accuracy).