دورية أكاديمية
Automatic classification and segmentation of blast cells using deep transfer learning and active contours.
| العنوان: | Automatic classification and segmentation of blast cells using deep transfer learning and active contours. |
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| المؤلفون: | Ametefe DS; Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia., Sarnin SS; Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia., Ali DM; Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia., Ametefe GD; Department of Biotechnology, College of Science, Engineering and Technology, Osun State University, Osogbo, Nigeria., John D; College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Puncak Perdana, Malaysia., Aliu AA; College of Built Environment, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia., Zoreno Z; Faculty of Pharmaceutical Sciences, University of Jos, Jos, Nigeria. |
| المصدر: | International journal of laboratory hematology [Int J Lab Hematol] 2024 May 10. Date of Electronic Publication: 2024 May 10. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Blackwell Scientific Publications Country of Publication: England NLM ID: 101300213 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1751-553X (Electronic) Linking ISSN: 17515521 NLM ISO Abbreviation: Int J Lab Hematol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Oxford : Blackwell Scientific Publications, c2007- |
| مستخلص: | Introduction: Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. Methods: To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. Results: The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. Conclusion: The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics. (© 2024 John Wiley & Sons Ltd.) |
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| فهرسة مساهمة: | Keywords: Chan‐Vese model; active contours; acute lymphoblastic leukaemia (ALL); blast cell; deep transfer learning |
| تواريخ الأحداث: | Date Created: 20240510 Latest Revision: 20240510 |
| رمز التحديث: | 20240510 |
| DOI: | 10.1111/ijlh.14305 |
| PMID: | 38726705 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1751-553X |
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| DOI: | 10.1111/ijlh.14305 |