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PubMed Publications| Keyword search (4,164 papers available) |  |
"Zunair H" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets | Islam M; Zunair H; Mohammed N; | 38492455 ENCS |
| 2 | Quantifying imbalanced classification methods for leukemia detection | Depto DS; Rizvee MM; Rahman A; Zunair H; Rahman MS; Mahdy MRC; | 36516574 ENCS |
| 3 | Knowledge distillation approach towards melanoma detection | Khan MS; Alam KN; Dhruba AR; Zunair H; Mohammed N; | 35594685 CONCORDIA |
| 4 | Sharp U-Net: Depthwise convolutional network for biomedical image segmentation | Zunair H; Ben Hamza A; | 34348214 ENCS |
| 5 | A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset. | Rahman A, Zunair H, Reme TR, Rahman MS, Mahdy MRC | 33465520 ENCS |
| 6 | Melanoma detection using adversarial training and deep transfer learning. | Zunair H, Ben Hamza A | 32252036 CONCORDIA |
| Title: | Quantifying imbalanced classification methods for leukemia detection | ||||
| Authors: | Depto DS, Rizvee MM, Rahman A, Zunair H, Rahman MS, Mahdy MRC | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/36516574/ | ||||
| DOI: | 10.1016/j.compbiomed.2022.106372 | ||||
| Publication: | Computers in biology and medicine | ||||
| Keywords: | Adversarial training; Domain adaptation; Imbalanced classification; Leukemia classification; | ||||
| PMID: | 36516574 | Category: | Date Added: | 2022-12-15 | |
| Dept Affiliation: |
ENCS
1 Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka, 1229, Bangladesh. Electronic address: deponker.sarker@northsouth.edu. 2 Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka, 1229, Bangladesh; Texas Tech University, Lubbock, TX, United States of America. Electronic address: mashfiq.rizvee@northsouth.edu. 3 Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka, 1229, Bangladesh. Electronic address: aimon.rahman@northsouth.edu. 4 Concordia University, Montreal, QC, Canada. Electronic address: hasibzunair@gmail.com. 5 Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palasi, Dhaka 1205, Bangladesh. Electronic address: msrahman@cse.buet.ac.bd. 6 Department of Electrical and Computer Engineering, North South Un |
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Description: |
Uncontrolled proliferation of B-lymphoblast cells is a common characterization of Acute Lymphoblastic Leukemia (ALL). B-lymphoblasts are found in large numbers in peripheral blood in malignant cases. Early detection of the cell in bone marrow is essential as the disease progresses rapidly if left untreated. However, automated classification of the cell is challenging, owing to its fine-grained variability with B-lymphoid precursor cells and imbalanced data points. Deep learning algorithms demonstrate potential for such fine-grained classification as well as suffer from the imbalanced class problem. In this paper, we explore different deep learning-based State-Of-The-Art (SOTA) approaches to tackle imbalanced classification problems. Our experiment includes input, GAN (Generative Adversarial Networks), and loss-based methods to mitigate the issue of imbalanced class on the challenging C-NMC and ALLIDB-2 dataset for leukemia detection. We have shown empirical evidence that loss-based methods outperform GAN-based and input-based methods in imbalanced classification scenarios. |
