Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network

The applications of AI in the healthcare sector are increasing day by day. The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classific...

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Bibliographic Details
Published in:International journal of telemedicine and applications 2022-04, Vol.2022, p.4176982-8
Main Authors: Magotra, Varun, Rohil, Mukesh Kumar
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification
Configurations
Datasets
Deep learning
Erythrocytes
Feature extraction
Image analysis
Image classification
Lightweight
Machine learning
Malaria
Medical diagnosis
Medical imaging
Medical imaging equipment
Medical research
Model accuracy
Neural networks
Parasites
Technology application
Telemedicine
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Summary:The applications of AI in the healthcare sector are increasing day by day. The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. In our study, we propose a lightweight CNN, which requires less time to train, for identifying malaria parasitic red blood cells and distinguishing them from healthy red blood cells. To compare the accuracy of our model, we used transfer learning on two models, namely, the VGG-19 and the Inception v3. We train our model in three different configurations depending on the proportion of data being fed to the model for training. For all three configurations, our proposed model is able to achieve an accuracy of around 96%, which is higher than both the other models that we trained for the same three configurations. It shows that our model is able to perform better along with low computational requirements. Therefore, it can be used more efficiently and can be easily deployed for detecting malaria cells.
ISSN:1687-6415
1687-6423
DOI:10.1155/2022/4176982