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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2022/4176982 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558379135926272 |
|---|---|
| author | Varun Magotra Mukesh Kumar Rohil |
| author_facet | Varun Magotra Mukesh Kumar Rohil |
| author_sort | Varun Magotra |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3bc9fa66e75d4e46a4e9bd77ac96e814 |
| institution | Kabale University |
| issn | 1687-6423 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-3bc9fa66e75d4e46a4e9bd77ac96e8142025-02-03T01:32:34ZengWileyInternational Journal of Telemedicine and Applications1687-64232022-01-01202210.1155/2022/4176982Malaria Diagnosis Using a Lightweight Deep Convolutional Neural NetworkVarun Magotra0Mukesh Kumar Rohil1Department of Computer EngineeringDepartment of Computer Science and Information SystemsThe 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.http://dx.doi.org/10.1155/2022/4176982 |
| spellingShingle | Varun Magotra Mukesh Kumar Rohil Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network International Journal of Telemedicine and Applications |
| title | Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network |
| title_full | Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network |
| title_fullStr | Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network |
| title_full_unstemmed | Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network |
| title_short | Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network |
| title_sort | malaria diagnosis using a lightweight deep convolutional neural network |
| url | http://dx.doi.org/10.1155/2022/4176982 |
| work_keys_str_mv | AT varunmagotra malariadiagnosisusingalightweightdeepconvolutionalneuralnetwork AT mukeshkumarrohil malariadiagnosisusingalightweightdeepconvolutionalneuralnetwork |