STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN
Abstract. In the modern world, the problem of the prevalence of cancer remains quite widespread, including acute lymphoblastic leukemia. It is very important to diagnose such diseases in the early stages in order to prescribe timely treatment and achieve patient remission. However, the problem that...
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Odessa National Academy of Food Technologies
2024-12-01
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| Series: | Автоматизация технологических и бизнес-процессов |
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| Online Access: | https://journals.ontu.edu.ua/index.php/atbp/article/view/3011 |
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| author | P. Kravchenko I. Burlachenko |
| author_facet | P. Kravchenko I. Burlachenko |
| author_sort | P. Kravchenko |
| collection | DOAJ |
| description | Abstract. In the modern world, the problem of the prevalence of cancer remains quite widespread, including acute lymphoblastic leukemia. It is very important to diagnose such diseases in the early stages in order to prescribe timely treatment and achieve patient remission. However, the problem that remains to this day is that the diagnosis of the disease based on microscopic images of a human blood smear are manual. This method of diagnosis is prone to errors due to many factors, due to inattention, absence of a specialist in the locality, etc. Therefore, the need for automated data collection of microscope images and their analysis with high accuracy is urgent. Research into the creation of low-cost devices and the creation of neural network architectures that can control the process of analysis and disease detection. It is proposed to develop a network consisting of hardware and software capable of transferring acquired data from the microscope to cloud storage for further use by a convolutional neural network to classify human blood smear images to detect healthy or blast blood cells. A hardware and software complex has been developed for collecting and transferring values from the microscope to cloud storage. The main module for receiving and transferring data to the cloud is a Raspberry Pi single-board computer that works on Wi-Fi technology. In conclusion, the proposed system is capable of ensuring the effectiveness of diagnosing blood cancers not only of lymphoblastic leukemia, but also of other types. |
| format | Article |
| id | doaj-art-5fcecdf231c14ecb9fc5c3984bc5200d |
| institution | Kabale University |
| issn | 2312-3125 2312-931X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Odessa National Academy of Food Technologies |
| record_format | Article |
| series | Автоматизация технологических и бизнес-процессов |
| spelling | doaj-art-5fcecdf231c14ecb9fc5c3984bc5200d2025-01-27T15:58:25ZengOdessa National Academy of Food TechnologiesАвтоматизация технологических и бизнес-процессов2312-31252312-931X2024-12-01164384610.15673/atbp.v16i4.30113011STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNNP. Kravchenko0I. Burlachenko1Petro Mohyla Black Sea National University, Mykolaiv, UkrainePetro Mohyla Black Sea National University, Mykolaiv, UkraineAbstract. In the modern world, the problem of the prevalence of cancer remains quite widespread, including acute lymphoblastic leukemia. It is very important to diagnose such diseases in the early stages in order to prescribe timely treatment and achieve patient remission. However, the problem that remains to this day is that the diagnosis of the disease based on microscopic images of a human blood smear are manual. This method of diagnosis is prone to errors due to many factors, due to inattention, absence of a specialist in the locality, etc. Therefore, the need for automated data collection of microscope images and their analysis with high accuracy is urgent. Research into the creation of low-cost devices and the creation of neural network architectures that can control the process of analysis and disease detection. It is proposed to develop a network consisting of hardware and software capable of transferring acquired data from the microscope to cloud storage for further use by a convolutional neural network to classify human blood smear images to detect healthy or blast blood cells. A hardware and software complex has been developed for collecting and transferring values from the microscope to cloud storage. The main module for receiving and transferring data to the cloud is a Raspberry Pi single-board computer that works on Wi-Fi technology. In conclusion, the proposed system is capable of ensuring the effectiveness of diagnosing blood cancers not only of lymphoblastic leukemia, but also of other types.https://journals.ontu.edu.ua/index.php/atbp/article/view/3011convolutional neural networkacute lymphoblastic leukemiaimage processingstreamingcloud storageremote accesswi-fi technologysingle-board computer |
| spellingShingle | P. Kravchenko I. Burlachenko STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN Автоматизация технологических и бизнес-процессов convolutional neural network acute lymphoblastic leukemia image processing streaming cloud storage remote access wi-fi technology single-board computer |
| title | STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN |
| title_full | STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN |
| title_fullStr | STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN |
| title_full_unstemmed | STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN |
| title_short | STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN |
| title_sort | streaming microscopic image data to the cloud storage for detection of acute lymphoblastic leukemia using cnn |
| topic | convolutional neural network acute lymphoblastic leukemia image processing streaming cloud storage remote access wi-fi technology single-board computer |
| url | https://journals.ontu.edu.ua/index.php/atbp/article/view/3011 |
| work_keys_str_mv | AT pkravchenko streamingmicroscopicimagedatatothecloudstoragefordetectionofacutelymphoblasticleukemiausingcnn AT iburlachenko streamingmicroscopicimagedatatothecloudstoragefordetectionofacutelymphoblasticleukemiausingcnn |