Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia
Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strat...
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MDPI AG
2024-12-01
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| author | Mahwish Ilyas Muhammad Bilal Nadia Malik Hikmat Ullah Khan Muhammad Ramzan Anam Naz |
| author_facet | Mahwish Ilyas Muhammad Bilal Nadia Malik Hikmat Ullah Khan Muhammad Ramzan Anam Naz |
| author_sort | Mahwish Ilyas |
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| description | Medical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially in life-threatening diseases such as leukemia. Leukemia, a blood malignancy, is one of the most prevalent cancer types affecting both adults and children. It is caused by the rapid and uncontrolled growth of abnormal white blood cells in the bone marrow. This accumulation interferes with the production of normal blood cells, leading to a weakened immune deficiency, anemia, and bleeding disorders. Conventional leukemia diagnostic methods are time-consuming, manually intensive, and inefficient. This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. The purpose of blood cell detection is to precisely identify and classify diverse blood cells, detecting anomalies associated with blood cancers like leukemia. This supports early diagnosis and monitoring, which leads to more effective treatments and improved results for cancer patients. To accomplish this task, we use digital image processing techniques and then apply the convolutional neural network (CNN) deep learning algorithm to blood sample images. This research employs a multi-stage methodology, including data preparation, data preprocessing, feature extraction, and then classification. While our model is built on a typical CNN architecture, we make significant advances by using preprocessing techniques and hyperparameter tuning. We have modified its layers combination to include convolutional, pooling, and fully connected layers that are optimized for image characteristics. These layers are fine-tuned for better feature extraction and classification accuracy. This study showed that blood cell detection for diagnosing acute leukemia based on images had 99% accuracy and outperformed other advanced models, including DenseNet121, ResNet-50, Incep-tionv3, MobileNet, and EfficientNet. The comprehensive analysis of the results reveals the highest accuracy of leukemia detection as compared to existing studies in the relevant literature. |
| format | Article |
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| issn | 2078-2489 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-b4b457e733ef4753b2572bddbb5520df2025-08-20T02:00:23ZengMDPI AGInformation2078-24892024-12-01151278710.3390/info15120787Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with LeukemiaMahwish Ilyas0Muhammad Bilal1Nadia Malik2Hikmat Ullah Khan3Muhammad Ramzan4Anam Naz5Department of Computer Science, Faculty of Computing and IT, University of Sargodha, Sargodha 40100, PakistanDepartment of Pharmaceutical Outcomes and Policy, Malachowsky Hall for Data Science and Information Technology, University of Florida, Gainesville, FL 32611, USADepartment of Software Engineering, Faculty of Computing and IT, University of Sargodha, Sargodha 40100, PakistanDepartment of Information Technology, Faculty of Computing and IT, University of Sargodha, Sargodha 40100, PakistanDepartment of Software Engineering, Faculty of Computing and IT, University of Sargodha, Sargodha 40100, PakistanDepartment of Computer Science, Faculty of Computing and IT, University of Sargodha, Sargodha 40100, PakistanMedical diagnosis plays a critical role in the early detection and treatment of diseases by examining symptoms and supporting findings through advanced laboratory testing. Early and accurate diagnosis is essential for detecting medical problems and then prescribing the most effective treatment strategies, especially in life-threatening diseases such as leukemia. Leukemia, a blood malignancy, is one of the most prevalent cancer types affecting both adults and children. It is caused by the rapid and uncontrolled growth of abnormal white blood cells in the bone marrow. This accumulation interferes with the production of normal blood cells, leading to a weakened immune deficiency, anemia, and bleeding disorders. Conventional leukemia diagnostic methods are time-consuming, manually intensive, and inefficient. This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. The purpose of blood cell detection is to precisely identify and classify diverse blood cells, detecting anomalies associated with blood cancers like leukemia. This supports early diagnosis and monitoring, which leads to more effective treatments and improved results for cancer patients. To accomplish this task, we use digital image processing techniques and then apply the convolutional neural network (CNN) deep learning algorithm to blood sample images. This research employs a multi-stage methodology, including data preparation, data preprocessing, feature extraction, and then classification. While our model is built on a typical CNN architecture, we make significant advances by using preprocessing techniques and hyperparameter tuning. We have modified its layers combination to include convolutional, pooling, and fully connected layers that are optimized for image characteristics. These layers are fine-tuned for better feature extraction and classification accuracy. This study showed that blood cell detection for diagnosing acute leukemia based on images had 99% accuracy and outperformed other advanced models, including DenseNet121, ResNet-50, Incep-tionv3, MobileNet, and EfficientNet. The comprehensive analysis of the results reveals the highest accuracy of leukemia detection as compared to existing studies in the relevant literature.https://www.mdpi.com/2078-2489/15/12/787cancer detectiondeep learningacute leukemiaclassificationwhite blood cells |
| spellingShingle | Mahwish Ilyas Muhammad Bilal Nadia Malik Hikmat Ullah Khan Muhammad Ramzan Anam Naz Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia Information cancer detection deep learning acute leukemia classification white blood cells |
| title | Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia |
| title_full | Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia |
| title_fullStr | Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia |
| title_full_unstemmed | Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia |
| title_short | Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia |
| title_sort | using deep learning techniques to enhance blood cell detection in patients with leukemia |
| topic | cancer detection deep learning acute leukemia classification white blood cells |
| url | https://www.mdpi.com/2078-2489/15/12/787 |
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