Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network
Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Sig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/2/196 |
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| author | Tusneem A. Elhassan Mohd Shafry Mohd Rahim Mohd Hashim Siti Zaiton Tan Tian Swee Taqwa Ahmed Alhaj Abdulalem Ali Mahmoud Aljurf |
| author_facet | Tusneem A. Elhassan Mohd Shafry Mohd Rahim Mohd Hashim Siti Zaiton Tan Tian Swee Taqwa Ahmed Alhaj Abdulalem Ali Mahmoud Aljurf |
| author_sort | Tusneem A. Elhassan |
| collection | DOAJ |
| description | Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the “GT-DCAE WBC augmentation model”. In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the “two-stage DCAE-CNN atypical WBC classification model” (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model’s discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%. |
| first_indexed | 2024-03-09T13:03:49Z |
| format | Article |
| id | doaj.art-709db49fa7984b6b9bf4201f643700d5 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T13:03:49Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-709db49fa7984b6b9bf4201f643700d52023-11-30T21:51:18ZengMDPI AGDiagnostics2075-44182023-01-0113219610.3390/diagnostics13020196Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural NetworkTusneem A. Elhassan0Mohd Shafry Mohd Rahim1Mohd Hashim Siti Zaiton2Tan Tian Swee3Taqwa Ahmed Alhaj4Abdulalem Ali5Mahmoud Aljurf6School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaSchool of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaSchool of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaBioinspired Device and Tissue Engineering Research Group, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81300, Johor, MalaysiaSchool of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, MalaysiaFaculty of Information Technology, City University, Petaling Jaya 46100, Selangor Darul Ehsan, MalaysiaKing Faisal Specialist Hospital and Research Center, Riyadh 11564, Saudi ArabiaRecent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the “GT-DCAE WBC augmentation model”. In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the “two-stage DCAE-CNN atypical WBC classification model” (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model’s discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%.https://www.mdpi.com/2075-4418/13/2/196acute myeloid leukemiaatypical white blood cellsautoencoderCNNaugmentation |
| spellingShingle | Tusneem A. Elhassan Mohd Shafry Mohd Rahim Mohd Hashim Siti Zaiton Tan Tian Swee Taqwa Ahmed Alhaj Abdulalem Ali Mahmoud Aljurf Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network Diagnostics acute myeloid leukemia atypical white blood cells autoencoder CNN augmentation |
| title | Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network |
| title_full | Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network |
| title_fullStr | Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network |
| title_full_unstemmed | Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network |
| title_short | Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network |
| title_sort | classification of atypical white blood cells in acute myeloid leukemia using a two stage hybrid model based on deep convolutional autoencoder and deep convolutional neural network |
| topic | acute myeloid leukemia atypical white blood cells autoencoder CNN augmentation |
| url | https://www.mdpi.com/2075-4418/13/2/196 |
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