Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite
| Autor: | Suresh Chandra Satapathy, Tathagat Banerjee, Sibi Chakkaravarthy Sethuraman, Ajith Jubilson, Aditya Jain, S. Karthikeyan |
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| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Deep learning Overfitting medicine.disease Machine learning computer.software_genre Convolutional neural network Class (biology) Hardware and Architecture parasitic diseases Media Technology medicine Key (cryptography) Artificial intelligence Transfer of learning business computer Software Malaria Malarial parasites |
| Zdroj: | Multimedia Tools and Applications. 81:13237-13251 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2 % when compared to the state-of-the-art Convolutional Neural Network (CNN) models. |
| Databáze: | OpenAIRE |
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