Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation
Leukemia is one of the most terminal types of blood cancer, and many people suffer from it every year. White blood cells (WBCs) have a significant association with leukemia diagnosis. Research studies reported that leukemia brings changes in WBC count and morphology. WBC accurate segmentation enable...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9768176/ |
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| author | Nadeem Akram Sharjeel Adnan Muhammad Asif Syed Muhammad Ali Imran Muhammad Naveed Yasir Rizwan Ali Naqvi Dildar Hussain |
| author_facet | Nadeem Akram Sharjeel Adnan Muhammad Asif Syed Muhammad Ali Imran Muhammad Naveed Yasir Rizwan Ali Naqvi Dildar Hussain |
| author_sort | Nadeem Akram |
| collection | DOAJ |
| description | Leukemia is one of the most terminal types of blood cancer, and many people suffer from it every year. White blood cells (WBCs) have a significant association with leukemia diagnosis. Research studies reported that leukemia brings changes in WBC count and morphology. WBC accurate segmentation enables to detect morphology and WBC count which consequently helps in the diagnosis and prognosis of leukemia. Manual WBC assessment methods are tedious, subjective, and less accurate. To overcome these problems, we propose a multi-scale information fusion network (MIF-Net) for WBC segmentation. MIF-Net is a shallow architecture with internal and external spatial information fusion mechanisms. In WBC images, the cytoplasm is with low contrast compared to the background, whereas nuclei shape can be complex with an indistinctive boundary for some cases, therefore accurate segmentation becomes challenging. Spatial features in the initial layers of the network include fine boundary information and MIF-Net splits and propagates this boundary information on multi-scale for external information fusion. Multi-scale information fusion in our network helps in preserving boundary information and contributes to segmentation performance improvement. MIF-Net also uses internal information fusion after intervals for feature empowerment in different stages of the network. We evaluated our network for four publicly available datasets and achieved state-of-the-art segmentation performance. In addition, the proposed architecture exhibits superior computational efficiency by using only 2.67 million trainable parameters. |
| first_indexed | 2024-04-12T15:15:34Z |
| format | Article |
| id | doaj.art-c3a67bf892204409b92087c76c25cfaa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T15:15:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c3a67bf892204409b92087c76c25cfaa2022-12-22T03:27:37ZengIEEEIEEE Access2169-35362022-01-0110487474876010.1109/ACCESS.2022.31719169768176Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells SegmentationNadeem Akram0Sharjeel Adnan1Muhammad Asif2Syed Muhammad Ali Imran3Muhammad Naveed Yasir4https://orcid.org/0000-0003-0878-1676Rizwan Ali Naqvi5https://orcid.org/0000-0002-7473-8441Dildar Hussain6https://orcid.org/0000-0001-9007-6284Department of Primary and Secondary Health Care, Government of Pakistan, Lahore, PakistanDepartment of Primary and Secondary Health Care, Government of Pakistan, Lahore, PakistanDepartment of Primary and Secondary Health Care, Government of Pakistan, Lahore, PakistanDepartment of Computer Science, University of Agriculture, Faisalabad, PakistanDepartment of Computer Science, University of Narowal, Narowal, PakistanDepartment of Unmanned Vehicle Engineering, Sejong University, Seoul, South KoreaSchool of Computational Sciences, Korea Institute for Advanced Study (KIAS), Seoul, South KoreaLeukemia is one of the most terminal types of blood cancer, and many people suffer from it every year. White blood cells (WBCs) have a significant association with leukemia diagnosis. Research studies reported that leukemia brings changes in WBC count and morphology. WBC accurate segmentation enables to detect morphology and WBC count which consequently helps in the diagnosis and prognosis of leukemia. Manual WBC assessment methods are tedious, subjective, and less accurate. To overcome these problems, we propose a multi-scale information fusion network (MIF-Net) for WBC segmentation. MIF-Net is a shallow architecture with internal and external spatial information fusion mechanisms. In WBC images, the cytoplasm is with low contrast compared to the background, whereas nuclei shape can be complex with an indistinctive boundary for some cases, therefore accurate segmentation becomes challenging. Spatial features in the initial layers of the network include fine boundary information and MIF-Net splits and propagates this boundary information on multi-scale for external information fusion. Multi-scale information fusion in our network helps in preserving boundary information and contributes to segmentation performance improvement. MIF-Net also uses internal information fusion after intervals for feature empowerment in different stages of the network. We evaluated our network for four publicly available datasets and achieved state-of-the-art segmentation performance. In addition, the proposed architecture exhibits superior computational efficiency by using only 2.67 million trainable parameters.https://ieeexplore.ieee.org/document/9768176/Deep learningcomputer-assisted diagnosisleukemia diagnosisWBC countWBC segmentation |
| spellingShingle | Nadeem Akram Sharjeel Adnan Muhammad Asif Syed Muhammad Ali Imran Muhammad Naveed Yasir Rizwan Ali Naqvi Dildar Hussain Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation IEEE Access Deep learning computer-assisted diagnosis leukemia diagnosis WBC count WBC segmentation |
| title | Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation |
| title_full | Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation |
| title_fullStr | Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation |
| title_full_unstemmed | Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation |
| title_short | Exploiting the Multiscale Information Fusion Capabilities for Aiding the Leukemia Diagnosis Through White Blood Cells Segmentation |
| title_sort | exploiting the multiscale information fusion capabilities for aiding the leukemia diagnosis through white blood cells segmentation |
| topic | Deep learning computer-assisted diagnosis leukemia diagnosis WBC count WBC segmentation |
| url | https://ieeexplore.ieee.org/document/9768176/ |
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