A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images
The numerous rising infections and deaths of malaria and acute lymphoblastic leukaemia (ALL) highlights the urgent need for early, useful, and efficient diagnosis methods. Recently, the framework of artificial intelligence has been applied to minimize time-consuming tasks, to increase the accuracy...
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| Format: | Article |
| Language: | English |
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Vietnam Ministry of Science and Technology
2022-05-01
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| Series: | Vietnam Journal of Science, Technology and Engineering |
| Subjects: | |
| Online Access: | https://vietnamscience.vjst.vn/index.php/vjste/article/view/38 |
| _version_ | 1828047976717615104 |
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| author | Quyen Hoang Vo Xuan-Hieu Le Thanh-Hai Le Thi-Thu-Hien Pham |
| author_facet | Quyen Hoang Vo Xuan-Hieu Le Thanh-Hai Le Thi-Thu-Hien Pham |
| author_sort | Quyen Hoang Vo |
| collection | DOAJ |
| description |
The numerous rising infections and deaths of malaria and acute lymphoblastic leukaemia (ALL) highlights the urgent need for early, useful, and efficient diagnosis methods. Recently, the framework of artificial intelligence has been applied to minimize time-consuming tasks, to increase the accuracy and flexibility of clinical diagnoses, and to reduce the pressure on physicians, diagnosticians, and clinical experts. In this study, a detection system for malaria and ALL is proposed that utilizes blood smear microscopic images with the aid of deep learning algorithms to identify and classify these two diseases automatically. The blood smear microscopic images consist of 1503 ALL images, 891 malaria images, and 1503 normal images that were divided into a training, validation, and testing sets in ratios of 50, 25, and 25%, respectively. The proposed model was built into three stages including the first stage for segmentation-applied modified UNet pre-trained model, the second stage for classification based on the convolution neural network model, and the final stage for classification utilizing perceptron as the combining model. As a result, the proposed system provides an alternative and interpretable method to detect abnormal leukocytes for ALL and malaria-infected blood cells with a 93% overall accuracy including the detection rate for ALL of 95% and the detection rate for malaria of 92%.
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| first_indexed | 2024-04-10T18:50:16Z |
| format | Article |
| id | doaj.art-4ea59636d01346a69fa54620142297e2 |
| institution | Directory Open Access Journal |
| issn | 2525-2461 2615-9937 |
| language | English |
| last_indexed | 2024-04-10T18:50:16Z |
| publishDate | 2022-05-01 |
| publisher | Vietnam Ministry of Science and Technology |
| record_format | Article |
| series | Vietnam Journal of Science, Technology and Engineering |
| spelling | doaj.art-4ea59636d01346a69fa54620142297e22023-02-01T08:12:44ZengVietnam Ministry of Science and TechnologyVietnam Journal of Science, Technology and Engineering2525-24612615-99372022-05-0164110.31276/VJSTE.64(1).63-71A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic imagesQuyen Hoang Vo0Xuan-Hieu Le1Thanh-Hai Le2Thi-Thu-Hien Pham3School of Biomedical Engineering, International University; Vietnam National University, Ho Chi Minh citySchool of Biomedical Engineering, International University, Vietnam National University, Ho Chi Minh cityFaculty of Mechanical Engineering, Ho Chi Minh city University of Technology, Vietnam National University, Ho Chi Minh citySchool of Biomedical Engineering, International University, Vietnam National University, Ho Chi Minh city The numerous rising infections and deaths of malaria and acute lymphoblastic leukaemia (ALL) highlights the urgent need for early, useful, and efficient diagnosis methods. Recently, the framework of artificial intelligence has been applied to minimize time-consuming tasks, to increase the accuracy and flexibility of clinical diagnoses, and to reduce the pressure on physicians, diagnosticians, and clinical experts. In this study, a detection system for malaria and ALL is proposed that utilizes blood smear microscopic images with the aid of deep learning algorithms to identify and classify these two diseases automatically. The blood smear microscopic images consist of 1503 ALL images, 891 malaria images, and 1503 normal images that were divided into a training, validation, and testing sets in ratios of 50, 25, and 25%, respectively. The proposed model was built into three stages including the first stage for segmentation-applied modified UNet pre-trained model, the second stage for classification based on the convolution neural network model, and the final stage for classification utilizing perceptron as the combining model. As a result, the proposed system provides an alternative and interpretable method to detect abnormal leukocytes for ALL and malaria-infected blood cells with a 93% overall accuracy including the detection rate for ALL of 95% and the detection rate for malaria of 92%. https://vietnamscience.vjst.vn/index.php/vjste/article/view/38acute lymphoblastic leukaemiablood smear microscopic imagedeep learningmalaria |
| spellingShingle | Quyen Hoang Vo Xuan-Hieu Le Thanh-Hai Le Thi-Thu-Hien Pham A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images Vietnam Journal of Science, Technology and Engineering acute lymphoblastic leukaemia blood smear microscopic image deep learning malaria |
| title | A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images |
| title_full | A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images |
| title_fullStr | A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images |
| title_full_unstemmed | A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images |
| title_short | A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images |
| title_sort | deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images |
| topic | acute lymphoblastic leukaemia blood smear microscopic image deep learning malaria |
| url | https://vietnamscience.vjst.vn/index.php/vjste/article/view/38 |
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