Image classification using deep neural networks for malaria disease detection
Since the 19th century, Malaria has become a terrifying life-threating disease in most of the countries. Its been identified that five countries namely Nigeria with 25%, Congo with a ratio of 11%, Mozambique with ratio of 5%, India with ratio of 4% and Uganda with ratio of 4%. World Health Organizat...
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2023
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| author | Lydia E.L. Moses G.J. Sharmili N. Shankar K. Maseleno A. |
| author2 | 57196059278 |
| author_facet | 57196059278 Lydia E.L. Moses G.J. Sharmili N. Shankar K. Maseleno A. |
| author_sort | Lydia E.L. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Since the 19th century, Malaria has become a terrifying life-threating disease in most of the countries. Its been identified that five countries namely Nigeria with 25%, Congo with a ratio of 11%, Mozambique with ratio of 5%, India with ratio of 4% and Uganda with ratio of 4%. World Health Organization stated that above 90% of malaria death cases were recorded every year. Most of the Indian states like Odisha, Madhya Pradesh, Maharastra, northern countries, Chhattisgarh got affected by Malaria. India spotted death cases of malaria from millions to thousands that have reduced in recent years. Directorate of National vector Bore disease control program has started malaria control strategies using early case detection and treatments, vector control, protective measures against mosquito bites and management of Environment. The major challenge was to identify the disease at early stage. The key contributions avoid malaria disease is to provide antimalaria drugs, using indoor spray with residual insecticides, mosquito nets. For the treatment, medical technologies, deep learning architectures related to Convolutional Neural Networks to train and test performing different combinations for image classification using ResNet34 which helps patient go through prior examination for microscopic diagnosis. For patients examination, this paper considers Malaria Cell Images dataset with Parasitized and uninfected images. Thus, this clearly shows that one can easily identify person�s condition whether he is infected or uninfected by enabling open-source Artificial Intelligence. It shows the start-of-the-art accuracy by checking individual details. � 2019, Research Trend. All rights reserved. |
| format | Article |
| id | my.uniten.dspace-24913 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Research Trend |
| record_format | dspace |
| spelling | my.uniten.dspace-249132023-05-29T15:28:44Z Image classification using deep neural networks for malaria disease detection Lydia E.L. Moses G.J. Sharmili N. Shankar K. Maseleno A. 57196059278 55532461200 57191575400 56884031900 55354910900 Since the 19th century, Malaria has become a terrifying life-threating disease in most of the countries. Its been identified that five countries namely Nigeria with 25%, Congo with a ratio of 11%, Mozambique with ratio of 5%, India with ratio of 4% and Uganda with ratio of 4%. World Health Organization stated that above 90% of malaria death cases were recorded every year. Most of the Indian states like Odisha, Madhya Pradesh, Maharastra, northern countries, Chhattisgarh got affected by Malaria. India spotted death cases of malaria from millions to thousands that have reduced in recent years. Directorate of National vector Bore disease control program has started malaria control strategies using early case detection and treatments, vector control, protective measures against mosquito bites and management of Environment. The major challenge was to identify the disease at early stage. The key contributions avoid malaria disease is to provide antimalaria drugs, using indoor spray with residual insecticides, mosquito nets. For the treatment, medical technologies, deep learning architectures related to Convolutional Neural Networks to train and test performing different combinations for image classification using ResNet34 which helps patient go through prior examination for microscopic diagnosis. For patients examination, this paper considers Malaria Cell Images dataset with Parasitized and uninfected images. Thus, this clearly shows that one can easily identify person�s condition whether he is infected or uninfected by enabling open-source Artificial Intelligence. It shows the start-of-the-art accuracy by checking individual details. � 2019, Research Trend. All rights reserved. Final 2023-05-29T07:28:44Z 2023-05-29T07:28:44Z 2019 Article 2-s2.0-85075476143 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075476143&partnerID=40&md5=3af1047aac55883fff13691f62de58a6 https://irepository.uniten.edu.my/handle/123456789/24913 10 4 66 70 Research Trend Scopus |
| spellingShingle | Lydia E.L. Moses G.J. Sharmili N. Shankar K. Maseleno A. Image classification using deep neural networks for malaria disease detection |
| title | Image classification using deep neural networks for malaria disease detection |
| title_full | Image classification using deep neural networks for malaria disease detection |
| title_fullStr | Image classification using deep neural networks for malaria disease detection |
| title_full_unstemmed | Image classification using deep neural networks for malaria disease detection |
| title_short | Image classification using deep neural networks for malaria disease detection |
| title_sort | image classification using deep neural networks for malaria disease detection |
| url_provider | http://dspace.uniten.edu.my/ |