The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks
Open accessAbstract
Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, we deployed pretrained AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. We also compared the data sets with different color models to check the performance over different color images. For acute lymphoblastic leukemia detection, we achieved a sensitivity of 100%, specificity of 98.11%, and accuracy of 99.50%; and for acute lymphoblastic leukemia subtype classification the sensitivity was 96.74%, specificity was 99.03%, and accuracy was 96.06%. Unlike the standard methods, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.
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Lung pattern classification for interstitial lung diseases using a deep convolutional neural network
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Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
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“So lot of groups in medical imaging deployed DCNNs and they were able to achieve good accuracy.(10-15) As per our knowledge for leukemia detection only, Vogado et al(16) investigated a pretrained CNN for the features extraction and used ensemble classifiers for classification of normal and blast cells.”
MethodUnsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring
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Leucocyte classification for leukaemia detection using image processing techniques
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Cited by
Transfer learning for medical image classification: a literature review
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00793-7 · Cited by 965
Jones, O. T., Calanzani, N., Saji, S., Duffy, S. W., Emery, J., Hamilton, W., Singh, H., de Wit, N. J., & Walter, F. M. (2021) · Journal of Medical Internet Research · doi:10.2196/23483 · Cited by 65
Mohlman, J. S., Leventhal, S. D., Hansen, T., Kohan, J., Pascucci, V., & Salama, M. E. (2020) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaa001 · Cited by 50
Metadata
- Authors
- Shafique, S., & Tehsin, S.
- Venue
- Technology in Cancer Research & Treatment
- Publisher
- SAGE Publications
- Published
- 2018-01-01
- Volume
- 17
- Pages
- 1533033818802789-1533033818802789
- ISSN
- 1533-0338 (ISSN-L)
- 1533-0346
- OpenAlex
- W2893154092
- Cited by
- 355
- References
- 16
- FWCI
- 12.34
- Type
- journal article
- Language
- en
- Open access
- gold
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- Shafique, S., & Tehsin, S. (2018). Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks. Technology in Cancer Research & Treatment. https://doi.org/10.1177/1533033818802789
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