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Original Article
Leukemia Detection Using Deep Learning: A DenseNet201-Based Approach
Mohammed Ahmed Ali1
Dr. Mohd Rafi Ahmed2
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 13-19
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250505003
References
1. S. S. Rehman, M. N. U. Rehman, A. Abbas, S. Ullah, and M. A. Khan, “Classification of acute lymphoblastic leukemia using deep learning,” Computers in Biology and Medicine, vol. 139, p. 104927, Mar. 2021.
2. M. A. Khan, J. H. Shah, A. Sharif, and T. Saba, “A novel deep learning-based framework for the detection and classification of leukemia using microscopic blood images,” IEEE Access, vol. 7, pp. 149398–149409, 2019.
3. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017.
4. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.
5. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, 2017, pp. 2261–2269.
6. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 25, 2012, pp. 1097–1105.
7. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, 2017, pp. 1251–1258.
8. TensorFlow Developers, “TensorFlow: An end-to-end open source platform for machine learning,” [Online]. Available: https://www.tensorflow.org/
9. Python Software Foundation, “Python Language Reference, version 3.x,” [Online]. Available: https://www.python.org
10. J. Brownlee, Deep Learning for Computer Vision. Machine Learning Mastery, 2019.
2. M. A. Khan, J. H. Shah, A. Sharif, and T. Saba, “A novel deep learning-based framework for the detection and classification of leukemia using microscopic blood images,” IEEE Access, vol. 7, pp. 149398–149409, 2019.
3. G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017.
4. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.
5. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, 2017, pp. 2261–2269.
6. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 25, 2012, pp. 1097–1105.
7. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, 2017, pp. 1251–1258.
8. TensorFlow Developers, “TensorFlow: An end-to-end open source platform for machine learning,” [Online]. Available: https://www.tensorflow.org/
9. Python Software Foundation, “Python Language Reference, version 3.x,” [Online]. Available: https://www.python.org
10. J. Brownlee, Deep Learning for Computer Vision. Machine Learning Mastery, 2019.
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