3,113
Views
9
CrossRef citations to date
0
Altmetric
Research Article

Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer

&
Article: 2011688 | Received 03 May 2021, Accepted 23 Nov 2021, Published online: 08 Dec 2021

References

  • Amin, M. M., S. Kermani, A. Talebi, and M. G. Oghli. 2015. Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. Journal of Medical Signals and Sensors 5 (1):49. doi:10.4103/2228-7477.150428.
  • Angermueller, C., T. Parnamaa, L. Parts, and O. Stegle. 2016. Deep learning for computational biology. Molecular Systems Biology 12 (878):1–866. doi:10.15252/msb.20156651.
  • Bardou, D., K. Zhang, and S. M. Ahmad. 2018. Lung sounds classification using convolutional neural networks. Artificial Intelligence in Medicine 88:58–69. doi:10.1016/j.artmed.2018.04.008.
  • Blundell, C., J. Cornebise, K. Kavukcuoglu, and D. Wierstra (2015). Weight uncertainty in neural networks. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, ICML’15, Lille, France, 2015, pp. 1613–22. JMLR.org.
  • Butler, K. T., D. W. Davies, H. Cartwright, O. Isayev, A. Walsh, et al. 2018. Machine learning for molecular and materials science. Nature. 559(7715):547–55. doi:10.1038/s41586-018-0337-2.
  • Das, P. K., A. Pradhan, and S. Meher. 2021. Detection of acute lymphoblastic leukemia using machine learning techniques. In Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. Lecture Notes in Electrical Engineering, ed. E. S. Gopi, vol. 749, 25–437. Singapore: Springer.
  • Das, P. K., and S. Meher. 2021a. An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia. Expert Systems with Applications 183:115311. doi:10.1016/j.eswa.2021.115311.
  • Das, P. K., and S. Meher 2021b. Transfer learning-based automatic detection of acute lymphocytic leukemia. National Conference on Communications (NCC), 1–6.
  • Das, P. K., S. Meher, R. Panda, and A. Abraham. 2020. A review of automated methods for the detection of sickle cell disease. IEEE Reviews in Biomedical Engineering 13 309–324.
  • Das, P. K., S. Meher, R. Panda, and A. Abraham. 2021. An efficient blood-cell segmentation for the detection of hematological disorders. IEEE Transactions on Cybernetics 1–12. doi:10.1109/TCYB.2021.3062152.
  • Das, P., K. Jadoun, and S. Meher 2020.Detection and classification of acute lymphocytic leukemia EEE-HYDCON, 1–5.
  • Gal, Y., and Z. Ghahramani 2016a. Bayesian convolutional neural networks with Bernoulli approximate variational inference. In 4th International Conference on Learning Representations (ICLR) Caribe Hilton, San Juan, Puerto Rico.
  • Gal, Y., and Z. Ghahramani 2016b. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016, 1050–59.
  • Genovese, A., M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti 2021a. Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning. In Proc. of the 2021 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, ON, Canada, June 6-11, 2021, 1205–09.
  • Genovese, A., M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti 2021b. Histopathological transfer learning for Acute Lymphoblastic Leukemia detection. In Proc. of the 2021 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2021), June 18-20, 2021 Hong Kong, China, 1–6.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning, vol. 1. Cambridge, MA,: MIT press Cambridge.
  • Hinton, G. E., N. Srivastava, A. Krizhevsky, Ilya Sutskever, I., Salakhutdinov, R. R., et al. 2012a. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
  • Hinton, G., N. Srivastava, and K. Swersky. 2012b. Neural networks for machine learning. https://www.cs.toronto.edu/tijmen/csc321/slides/lecture_slides_lec6.pdf.
  • Hunger, S. P., C. G. Mullighan, and D. L. Longo. 2015. Acute lymphoblastic leukemia in children. New England Journal of Medicine 373 (16):1541–52. doi:10.1056/NEJMra1400972.
  • Husham, A., M. H. Alkawaz, T. Saba, A. Rehman, J. Saleh Alghamdi, et al. 2016. Automated nuclei segmentation of malignant using level sets. Microscopy Research and Technique. 79(10):993–97. doi:10.1002/jemt.22733.
  • Iqbal, S., M. U. Ghani, T. Saba, A. Rehman, and P. Saggau. 2018. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microscopy Research and Technique 81 (4):419–27. doi:10.1002/jemt.22994.
  • Labati, R. D., V. Piuri, and F. Scotti. 2011. All-idb: The acute lymphoblastic leukemia image database for image processing. In 18th IEEE international conference on Image processing (ICIP), 2011 Brussels, Belgium, 2045–48.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436–44. doi:10.1038/nature14539.
  • Li, J., M. Gao, R. D'Agostino, et al. 2020. Evaluating classification accuracy for modern learning approaches. Cell. 180(4):688–702. doi:10.1016/j.cell.2020.01.021.
  • Li, J., M. Gao, and R. D’Agostino. 2019. Evaluating classification accuracy for modern learning approaches. Statistics in Medicine 38 (13):2477–503. doi:10.1002/sim.8103.
  • Masud, M., A. Eldin Rashed, and M. Hossain. 2020. Convolutional neural network-based models for diagnosis of breast cancer. Neural Computing and Applications 1–12.
  • McLachlan, S., K. Dube, G. A. Hitman, N. E. Fenton, E. Kyrimi, et al. 2020. Bayesian networks in healthcare: Distribution by medical condition. Artificial Intelligence in Medicine 107:101912. doi:10.1016/j.artmed.2020.101912.
  • Mohapatra, S., D. Patra, and S. Satpathy. 2014. An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Computing & Applications 24 (7–8):1887–904. doi:10.1007/s00521-013-1438-3.
  • MoradiAmin, M., A. Memari, N. Samadzadehaghdam, S. Kermani, A. Talebi, et al. 2016. Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis. Microscopy Research and Technique. 79(10):908–16. doi:10.1002/jemt.22718.
  • NCI. 2018. National Cancer Institute U.S.A.
  • Putzu, L., G. Caocci, and C. Di Ruberto. 2014. Leucocyte classification for leukaemia detection using image processing techniques. Artificial Intelligence in Medicine 62 (3):179–91. doi:10.1016/j.artmed.2014.09.002.
  • Rawat, J., A. Singh, H. S. Bhadauria, J. Virmani, J. S. Devgun, et al. 2017. Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimedia Tools and Applications. 76(18):19057–85. doi:10.1007/s11042-017-4478-3.
  • Scotti, F. 2005. Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005, Messian, Italy, pp. 96–101.
  • Singh, G., G. Bathla, and S. Kaur. 2016. Design of new architecture to detect leukemia cancer from medical images. International Journal of Applied Engineering Research 11 (10):7087–94.
  • Srivastava, N., G. Hinton, A. Krizhevsky, Sutskever, I., Salakhutdinov, R., et al. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15 (1):1929–58.
  • Steingrimsson, J. A., and S. Morrison. 2020. Deep learning for survival outcomes. Statistics in Medicine 39 (17):2339–49. doi:10.1002/sim.8542.
  • Tikhonov, A. 1963. Solution of incorrectly formulated problems and the regularization method. Soviet Mathematics - Doklady 4:1035–38.
  • Vos, T., C. Allen, M. Arora, R. M. Barber, et al. 2016. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the global burden of disease study 2015. The Lancet 388 (10053):1545–602.
  • Zang, N., Y. Cia, Y. Wang, Y. Tian, X. Wang, and B. Badami. 2020. Skin cancer diagnosis based on optimized convolutional neural network. Artificial Intelligence in Medicine 102:1–7.