An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images
Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to m...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024021807 |
| _version_ | 1797267710341545984 |
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| author | Riaz Ullah Khan Sultan Almakdi Mohammed Alshehri Amin Ul Haq Aman Ullah Rajesh Kumar |
| author_facet | Riaz Ullah Khan Sultan Almakdi Mohammed Alshehri Amin Ul Haq Aman Ullah Rajesh Kumar |
| author_sort | Riaz Ullah Khan |
| collection | DOAJ |
| description | Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis. |
| first_indexed | 2024-03-08T00:23:27Z |
| format | Article |
| id | doaj.art-f6e72d62642e4b43bb2da15ce9cfe6d9 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:20:55Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-f6e72d62642e4b43bb2da15ce9cfe6d92024-03-09T09:27:22ZengElsevierHeliyon2405-84402024-02-01104e26149An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical imagesRiaz Ullah Khan0Sultan Almakdi1Mohammed Alshehri2Amin Ul Haq3Aman Ullah4Rajesh Kumar5Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China; Corresponding author.Department of Computer Science, College of Computer Science and Information systems, Najran University, Najran 55461, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information systems, Najran University, Najran 55461, Saudi ArabiaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, ChinaYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, ChinaBiomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.http://www.sciencedirect.com/science/article/pii/S2405844024021807RBC detectionObject detectionImage processingDeep learning |
| spellingShingle | Riaz Ullah Khan Sultan Almakdi Mohammed Alshehri Amin Ul Haq Aman Ullah Rajesh Kumar An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images Heliyon RBC detection Object detection Image processing Deep learning |
| title | An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images |
| title_full | An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images |
| title_fullStr | An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images |
| title_full_unstemmed | An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images |
| title_short | An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images |
| title_sort | intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images |
| topic | RBC detection Object detection Image processing Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024021807 |
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