An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like bloo...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10662 |
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| author | Muhammad Zakir Ullah Yuanjie Zheng Jingqi Song Sehrish Aslam Chenxi Xu Gogo Dauda Kiazolu Liping Wang |
| author_facet | Muhammad Zakir Ullah Yuanjie Zheng Jingqi Song Sehrish Aslam Chenxi Xu Gogo Dauda Kiazolu Liping Wang |
| author_sort | Muhammad Zakir Ullah |
| collection | DOAJ |
| description | Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists. |
| first_indexed | 2024-03-10T05:44:21Z |
| format | Article |
| id | doaj.art-9a05545bd79d4853b6cca2a180d0d0a7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:21Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9a05545bd79d4853b6cca2a180d0d0a72023-11-22T22:17:07ZengMDPI AGApplied Sciences2076-34172021-11-0111221066210.3390/app112210662An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia ClassificationMuhammad Zakir Ullah0Yuanjie Zheng1Jingqi Song2Sehrish Aslam3Chenxi Xu4Gogo Dauda Kiazolu5Liping Wang6School of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaLeukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.https://www.mdpi.com/2076-3417/11/22/10662acute lymphoblastic leukemiamedical image classificationconvolutional neural networksefficient channel attention |
| spellingShingle | Muhammad Zakir Ullah Yuanjie Zheng Jingqi Song Sehrish Aslam Chenxi Xu Gogo Dauda Kiazolu Liping Wang An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification Applied Sciences acute lymphoblastic leukemia medical image classification convolutional neural networks efficient channel attention |
| title | An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification |
| title_full | An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification |
| title_fullStr | An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification |
| title_full_unstemmed | An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification |
| title_short | An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification |
| title_sort | attention based convolutional neural network for acute lymphoblastic leukemia classification |
| topic | acute lymphoblastic leukemia medical image classification convolutional neural networks efficient channel attention |
| url | https://www.mdpi.com/2076-3417/11/22/10662 |
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