MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps
This work addresses malaria diagnosis in remote areas with limited medical facilities, though it is incremental as it builds on existing CNN-based CADx techniques.
The authors tackled malaria diagnosis by developing a lightweight deep learning model, Mosquito-Net, to classify infected and uninfected cells in microscopic blood smears, achieving deployment capability on edge and mobile devices with fewer parameters and less computation power.
Malaria is considered one of the deadliest diseases in today world which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium which infects the red blood cells in human beings. The parasites are transmitted by a female class of mosquitos known as Anopheles. The diagnosis of malaria requires identification and manual counting of parasitized cells by medical practitioners in microscopic blood smears. Due to the unavailability of resources, its diagnostic accuracy is largely affected by large scale screening. State of the art Computer-aided diagnostic techniques based on deep learning algorithms such as CNNs, with end to end feature extraction and classification, have widely contributed to various image recognition tasks. In this paper, we evaluate the performance of custom made convnet Mosquito-Net, to classify the infected and uninfected cells for malaria diagnosis which could be deployed on the edge and mobile devices owing to its fewer parameters and less computation power. Therefore, it can be wildly preferred for diagnosis in remote and countryside areas where there is a lack of medical facilities.