sowmyakasu/malaria-detection

This study explores automated malaria detection using blood cell images. Models like CNN, RCNN, Transfer Learning, GNB, and a stacked ensemble were trained on 20,000 images. Evaluated using accuracy, precision, recall, and F1-score, results show deep learning's potential to improve diagnosis in low-resource settings. # malaria-detection This study explores automated malaria detection using blood cell images. Models like CNN, RCNN, Transfer Learning, GNB, and a stacked ensemble were trained on 20,000 images. Evaluated using accuracy, precision, recall, and F1-score, results show deep learning's potential to improve diagnosis in low-resource settings. Author-SowmyaKasu