Transfer Learning For White Blood Cell Leukemia Detection In Image Processing
Keywords:
Cells (biology), Deep learning, Transfer learning, White blood cell (WBC)Abstract
Leukemia, a type of blood cancer, requires accurate and early detection for effective treatment. White blood cell (WBC) abnormalities play a crucial role in diagnosing leukemia. However, manual examination of blood smears to detect abnormal WBCs is time-consuming and prone to human error. Transfer learning, a technique that leverages pre-trained deep neural networks, has shown promise in improving the accuracy and efficiency of leukemia detection. In this study, author explore the application of transfer learning using three popular deep neural network architectures: InceptionV3, Squeeze Net, and ResNet50, for WBC detection in leukemia. Author utilize a large dataset of microscopic blood smear images, consisting of both normal and abnormal WBCs, to train and fine-tune these pre-trained networks. By leveraging the knowledge and features learned from massive image datasets, transfer learning enables us to effectively extract relevant features from the blood smear images and enhance the accuracy of leukemia detection. The trained models are evaluated on a separate test set of blood smear images, and their performance metrics, including accuracy, precision, recall, and F1-score, are measured and compared. Additionally, we assess the computational efficiency of the models in terms of inference time, which is crucial for real-time diagnosis.
Downloads
References
Amin Salih Mohammed Hersh A. Muhamad , Shahab Wahhab Kareem, 2022. A Deep Learning Method for Detecting Leukemia in Real Images. NeuroQuantology.
Ahmad, R.; Awais, M.; Kausar, N.; Akram, T. White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization. Diagnostics 2023, 13, 352. https://doi.org/10.3390/diagnostics13030352
H. A. Muhamad, S. W. Kareem and A. S. Mohammed, 2022. A Comparative Evaluation of Deep Learning Methods in Automated Classification of White Blood Cell Images. Erbil, IEEE, pp. 205-211, doi: 10.1109/IEC54822.2022.9807456.
Sami H. Ismael, Shahab Wahhab Kareem, Firas H. Almukhtar, "Medical Image Classification Using Different Machine Learning Algorithms,", AL-Rafidain Journal of Computer Sciences and Mathematics, pp. 135-147, 1 14 2020.
S. W. Kareem, "An Evaluation ALgorithms for Classifying Leukocytes Images," in 7th International Engineering Conference Research &Innovation amid Global Pandemic (IEC2021) Erbil, Iraq, 67-72,2021.
Roojwan Sc Hawezi, Farah Sami Khoshaba, Shahab Wahhab Kareem, A comparison of automated classification techniques for image processing in video internet of things, omputers and Electrical Engineering, Volume 101,2022,108074.
Kermany DS, Goldbaum M, Cai W, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018;172(5):1122-1131.e9. doi:10.1016/j.cell.2018.02.010
Tajbakhsh N, Shin JY, Gurudu SR, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging. 2016;35(5):1299-1312. doi:10.1109/TMI.2016.2535302
Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems. 2014:3320-3328.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056
Albahri, O.S., Almahbashi, A.M., Albahri, A.S., Zaidan, A.A., & Zaidan, B.B. (2018). Leukemia blood cells classification using image processing. Journal of Healthcare Engineering, 2018, 1-17.
Alshahrani, M., Alalwan, H., Alsolami, R., Alzahrani, S., & Alsaqer, M. (2018). Acute Leukemia Detection Using Image Processing Techniques. International Journal of Advanced Computer Science and Applications, 9(9), 132-139.
Biswas, M., & Akhand, M.A.H. (2020). Leukemia Classification using Deep Convolutional Neural Network. 2020 International Conference on Networking, Systems and Security (NSysS), Dhaka, Bangladesh, 1-6.
Chen, W., Gao, X., Yao, L., Yan, X., & Zhang, L. (2018). Leukemia Classification and Recognition Based on Convolutional Neural Network. In Proceedings of the 2018 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China, 301-305.
Dey, S., Chakraborty, S., Dutta, S., & Chattopadhyay, S. (2020). Efficient Classification of Leukemia Cells in Microscopic Images Using Transfer Learning. 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 642-647.
Hossain, M.S., Al-Nafjan, A., Saeed, F., & Mahmood, S. (2019). Leukemia Detection using Convolutional Neural Network. 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend-on-Sea, UK, 1-6.
Islam, M.R., Al-Kabbany, A., Salim, N., & Haque, M.N. (2019). White Blood Cell Detection in Leukemia Using Image Processing Techniques. 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 410-415.
Khan, A., Zafar, A., Razzak, M.I., & Naz, S. (2019). Automated Detection of Leukemia using Deep Learning Techniques. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 255-260.
Khan, R., Qamar, A., & Ahmad, A. (2019). Leukemia Detection using Deep Learning Techniques: A Comprehensive Study. 2019 International Conference on Information Networking (ICOIN), Kuala Lumpur, Malaysia, 10-15.
Khatun, A., Eftekhari, M., & Parvin, S. (2017). Leukemia Detection Using Fuzzy K-Nearest Neighbor Classification. 2017 3rd International Conference on Electrical Information and Communication Technologies (EICT), Khulna, Bangladesh, 1-5.
Kumar, R., Mittal, R., & Sharma, V. (2021). A Comparative Study of Leukemia Detection using Deep Learning Techniques. 2021 IEEE International Conference on Advances in Computing, Communication and Information Technology (ICACCI), Raipur, India, 1-6.
Mishra, R., Prakash, O., & Kumar, R. (2018). Detection of Leukemia from Blood Smear Images Using Machine Learning Techniques. 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), Mumbai, India, 1-6.
Paul, A.K., Rahman, M.H., Ferdous, S., & Reza, M.S. (2019). Classification of White Blood Cell Images for Leukemia Detection Using Deep Learning Techniques. 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 1-6.
Roy, P.P., Bhattacharjee, D., & Panwar, M. (2020). Leukemia Detection and Classification using Deep Learning Techniques: A Review. 2020 International Conference on Smart Electronics and Communication (ICOSEC), Puducherry, India, 1-6.
Sharma, S., Goyal, R., & Jain, S. (2021). Classification of Leukemia Using Transfer Learning Based Deep Neural Networks. 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 542-546.
N. Ahmed, A. Yigit, Z. Isik, and A. Alpkocak, “Identification of leukemia subtypes from microscopic images using convolutional neural network,” Diagnostics, vol. 9, no. 3, p. 104, 2019.
M. Tuba and E. Tuba, “Generative adversarial optimization (Goa) for acute lymphocytic leukemia detection,” Studies in Informatics and Control, vol. 28, no. 3, pp. 245–254, 2019.
S. Agaian, M. Madhukar, and A. T. Chronopoulos, “A new acute leukaemia-automated classification system,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 6, no. 3, pp. 303–314, 2018.
A.Ibrahim, A.Khalid, O. Alhabashneh, E. Nadeem, M. Almajali, F. Harasees “Design a New Neural Network Architecture Using aLayer of Neurons”, International Journal on Recent and Innovation Trends in Computing and CommunicationISSN: 2321-8169 Volume: 11 Issue: 9, 2023.
A.Khalid, “Automated System for Constructing a NeuralNetwork by Back Propagation Error” NeuroQuantology |January 2023 | Volume 21 | Issue 2 |Page 625-629| Doi: 10.48047/NQ.2023.21.2.NQ23065, 2023.
A.Khalid, “Hybrid Convolution neural Network and Graph Neural network for Detection of Leukocyte” International Journal on Recent and Innovation Trends in Computing and Communication, 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
