Malaria Disease Prediction Based on Convolutional Neural Networks
This study delves into the investigation of the efficacy of Convolutional Neural Networks (CNNs) in identifying malaria through the examination of cell images. The dataset employed encompasses a total of 27,558 images, harvested from the renowned Malaria Cell Images Dataset on Kaggle, encompassing...
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2024-06-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | https://yrpipku.com/journal/index.php/jaets/article/view/3947 |
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| author | Dhrgam AL Kafaf Noor N. Thamir Samara S. AL-Hadithy |
| author_facet | Dhrgam AL Kafaf Noor N. Thamir Samara S. AL-Hadithy |
| author_sort | Dhrgam AL Kafaf |
| collection | DOAJ |
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This study delves into the investigation of the efficacy of Convolutional Neural Networks (CNNs) in identifying malaria through the examination of cell images. The dataset employed encompasses a total of 27,558 images, harvested from the renowned Malaria Cell Images Dataset on Kaggle, encompassing cells of diverse nature. The architectonics of the CNN model is meticulously devised, comprising of six blocks and three interconnected blocks, thereby rendering an efficient extraction of features and subsequent classification of the cells. Creative paraphrasing: Various strategies such as dropout, batch normalization, and global average pooling are artfully utilized to refine and fortify the model, ensuring its robustness and adaptability. In order to confront the challenge of diminishing gradient and facilitate the attainment of convergence, the activation function known as Rectified Linear Unit (ReLU) is ingeniously employed. Assessing the efficacy of the model via a perplexity grid produces outcomes. Exhibiting a precision rate of 99.59%, a specificity measure of 99.69%, an Sensitivity of 99.40%, F1 Measurement of 99.44%, and a Precision of 99.48, it showcases its capacity to effectively distinguish betwixt malaria-afflicted cells and unafflicted cells. The focal point of this research highlights the substantial potential of CNNs in facilitating the automated identification of malaria using image analysis. By harnessing their unique architecture and regularization techniques, CNNs have the capability to enhance the results and potentially bring about better outcomes in areas with prevalent cases of malaria.
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| format | Article |
| id | doaj-art-8d9454ef6be34210a838b6d7d8d2bea7 |
| institution | Kabale University |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-8d9454ef6be34210a838b6d7d8d2bea72025-01-03T12:32:22ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792024-06-015210.37385/jaets.v5i2.3947Malaria Disease Prediction Based on Convolutional Neural NetworksDhrgam AL Kafaf0Noor N. Thamir1Samara S. AL-Hadithy2College of Art and Science, Department of Natural & Applied Sciences, American University of Iraq – BaghdadMinistry of education, General Directorate of Education of Rusafa II, Baghdad, IraqMinistry of Education, Karkh First Directorate of Education, Baghdad, Iraq This study delves into the investigation of the efficacy of Convolutional Neural Networks (CNNs) in identifying malaria through the examination of cell images. The dataset employed encompasses a total of 27,558 images, harvested from the renowned Malaria Cell Images Dataset on Kaggle, encompassing cells of diverse nature. The architectonics of the CNN model is meticulously devised, comprising of six blocks and three interconnected blocks, thereby rendering an efficient extraction of features and subsequent classification of the cells. Creative paraphrasing: Various strategies such as dropout, batch normalization, and global average pooling are artfully utilized to refine and fortify the model, ensuring its robustness and adaptability. In order to confront the challenge of diminishing gradient and facilitate the attainment of convergence, the activation function known as Rectified Linear Unit (ReLU) is ingeniously employed. Assessing the efficacy of the model via a perplexity grid produces outcomes. Exhibiting a precision rate of 99.59%, a specificity measure of 99.69%, an Sensitivity of 99.40%, F1 Measurement of 99.44%, and a Precision of 99.48, it showcases its capacity to effectively distinguish betwixt malaria-afflicted cells and unafflicted cells. The focal point of this research highlights the substantial potential of CNNs in facilitating the automated identification of malaria using image analysis. By harnessing their unique architecture and regularization techniques, CNNs have the capability to enhance the results and potentially bring about better outcomes in areas with prevalent cases of malaria. https://yrpipku.com/journal/index.php/jaets/article/view/3947Convolutional Neural NetworkMalaria diseaseComputer Aides DetectionDeep Learning |
| spellingShingle | Dhrgam AL Kafaf Noor N. Thamir Samara S. AL-Hadithy Malaria Disease Prediction Based on Convolutional Neural Networks Journal of Applied Engineering and Technological Science Convolutional Neural Network Malaria disease Computer Aides Detection Deep Learning |
| title | Malaria Disease Prediction Based on Convolutional Neural Networks |
| title_full | Malaria Disease Prediction Based on Convolutional Neural Networks |
| title_fullStr | Malaria Disease Prediction Based on Convolutional Neural Networks |
| title_full_unstemmed | Malaria Disease Prediction Based on Convolutional Neural Networks |
| title_short | Malaria Disease Prediction Based on Convolutional Neural Networks |
| title_sort | malaria disease prediction based on convolutional neural networks |
| topic | Convolutional Neural Network Malaria disease Computer Aides Detection Deep Learning |
| url | https://yrpipku.com/journal/index.php/jaets/article/view/3947 |
| work_keys_str_mv | AT dhrgamalkafaf malariadiseasepredictionbasedonconvolutionalneuralnetworks AT noornthamir malariadiseasepredictionbasedonconvolutionalneuralnetworks AT samarasalhadithy malariadiseasepredictionbasedonconvolutionalneuralnetworks |