Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone
This provides an incremental improvement for healthcare in malaria-endemic regions by enhancing diagnostic efficiency with a computer-aided system.
The paper tackled the problem of automating malaria diagnosis from blood smear images to address manual detection's time and accuracy issues, achieving high performance with 98.75% accuracy, 99.3% precision, and 99.5% recall using a ResNet50-based model.
According to the World Malaria Report of 2022, 247 million cases of malaria and 619,000 related deaths were reported in 2021. This highlights the predominance of the disease, especially in the tropical and sub-tropical regions of Africa, parts of South-east Asia, Central and Southern America. Malaria is caused due to the Plasmodium parasite which is circulated through the bites of the female Anopheles mosquito. Hence, the detection of the parasite in human blood smears could confirm malarial infestation. Since the manual identification of Plasmodium is a lengthy and time-consuming task subject to variability in accuracy, we propose an automated, computer-aided diagnostic method to classify malarial thin smear blood cell images as parasitized and uninfected by using the ResNet50 Deep Neural Network. In this paper, we have used the pre-trained ResNet50 model on the open-access database provided by the National Library of Medicine's Lister Hill National Center for Biomedical Communication for 150 epochs. The results obtained showed accuracy, precision, and recall values of 98.75%, 99.3% and 99.5% on the ResNet50(proposed) model. We have compared these metrics with similar models such as VGG16, Watershed Segmentation and Random Forest, which showed better performance than traditional techniques as well.