Efficient Diagnosis of Acute Lymphoblastic Leukemia using Transfer Learning
Keywords:
Leukemia, CNN, ALL, machine learning, microscopic imagesAbstract
Leukemia, as the most prevailing form of blood cancer, impacts both adults and children. This demands timely detection for effective intervention. However, the traditional manual diagnostic methods suffer from time-consuming processes and are prone to skill-dependent variations. In our work, we employ transfer learning techniques, which leverage information gained from models trained on enormous scale datasets, resulting in significant reductions in the need for large amount of labeled data as well as computational resources for the specialized task of ALL detection, thereby improving efficiency in terms of both time and cost. This study proposes an innovative automated Leukemia detection system utilizing advanced Machine Learning (ML) techniques, particularly deep learning-based Convolutional Neural Networks (CNNs) and Transfer Learning. Here we have proposed a model with CNN layers added to Transfer Learning Architectures in which the model with EfficientNetB3 gives the best results with a Training Accuracy of 100% and Testing Accuracy of 96.87%, F1-Score of 96.9%, Recall of 96.24% and precision of 97.58% making it the one of the promising model among other evaluated CNN architectures earlier on C-NMC-2019 ALL Dataset. The proposed system addresses the crucial need for early leukemia diagnosis. It overcomes the drawbacks of manual methods by efficiently analyzing microscopic images, extracting essential features, and applying filtering techniques to enhance accuracy. This automated approach promises to improve blood cancer detection by providing an accurate tool for clinicians and healthcare professionals, thereby significantly contributing to the enhanced patient care and management of ALL.
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J. Sheet, C. Ghosh and B. K. Das, "Deep Learning-Based Transfer Learning for the Detection of Leukemia," 2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC), Silchar, India, 2023, pp. 1-6, doi: 10.1109/ISACC56298.2023.10084138.
Gundepudi, Surya & Jain, Charu & Narasimhan, Venkateswaran. (2021). Detection of Acute Lymphoblastic Leukemia by Utilizing Deep Learning methods.
Raina, Rohini & Gondhi, Naveen Kumar & Chaahat, & Singh, Dilbag & Kaur, Manjit & Lee, Heung-No. (2022). A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques. Archives of Computational Methods in Engineering. 30. 10.1007/s11831-022-09796-7.
P. Das, B. Sahoo and S. Meher, "An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model" in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 03, pp. 1817-1828, 2023.
R. Ribani and M. Marengoni, "A Survey of Transfer Learning for Convolutional Neural Networks," 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), Rio de Janeiro, Brazil, 2019, pp. 47-57, doi: 10.1109/SIBGRAPI-T.2019.00010.
M. Chandrasekar, M. Ganesh, B. Saleena and P. Balasubramanian, "Breast Cancer Histopathological Image Classification using EfficientNet Architecture," 2020 IEEE International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), Bengaluru, India, 2020, pp. 1-5, doi: 10.1109/TEMSMET51618.2020.9557441.
] Sampathila, N.; Chadaga, K.; Goswami, N.; Chadaga, R.P.; Pandya, M.; Prabhu, S.; Bairy, M.G.; Katta, S.S.; Bhat, D.; Upadya, S.P. Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images. Healthcare 2022, 10, 1812. https://doi.org/10.3390/healthcare10101812.
] M. O. Aftab, M. Javed Awan, S. Khalid, R. Javed and H. Shabir, "Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning," 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), Riyadh, Saudi Arabia, 2021, pp. 216-220, doi: 10.1109/CAIDA51941.2021.9425264.
Oliveira, José & Dantas, Daniel. (2021). Classification of Normal versus Leukemic Cells with Data Augmentation and Convolutional Neural Networks. 685-692. 10.5220/0010257406850692.
Kasani PH, Park SW, Jang JW. An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification. Diagnostics (Basel). 2020 Dec 8;10(12):1064. doi: 10.3390/diagnostics10121064. PMID: 33302591; PMCID: PMC7763941.
Rehman A, Abbas N, Saba T, Rahman Syed Ijaz ur, Mehmood Z, Kolivand H. Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech. 2018; 81: 1310–1317. https://doi.org/10.1002/jemt.23139
Shafique S, Tehsin S. Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks. Technology in Cancer Research & Treatment. 2018;17. doi:10.1177/1533033818802789
Vogado, Luis Henrique & Veras, Rodrigo & Andrade, Alan & Araújo, Flávio Henrique & Silva, Romuere & Aires, Kelson. (2017). Diagnosing Leukemia in Blood Smear Images Using an Ensemble of Classifiers and Pre-Trained Convolutional Neural Networks. 10.1109/SIBGRAPI.2017.55.
W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/10.1016/j.neucom.2016.12.038.
Prellberg, J., Kramer, O. (2019). Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_6.
Honnalgere, A., Nayak, G. (2019). Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_1.
Dig Vijay Kumar Yarlagadda, Praveen Rao, Deepthi Rao, and Ossama Tawfik "A system for one-shot learning of cervical cancer cell classification in histopathology images", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095611 (18 March 2019); https://doi.org/10.1117/12.2512963.
Alharbi AH, Aravinda CV, Lin M, Venugopala PS, Reddicherla P, Shah MA. Segmentation and Classification of White Blood Cells Using the UNet. Contrast Media Mol Imaging. 2022 Jul 11;2022:5913905. doi: 10.1155/2022/5913905. PMID: 35919503; PMCID: PMC9293541.
Yentrapragada, D. Deep features based convolutional neural network to detect and automatic classification of white blood cells. J Ambient Intell Human Comput 14, 9191–9205 (2023). https://doi.org/10.1007/s12652-022-04422-7.
Munir, Muhammad Asif & Aslam, Muhammad & Shafique, Muhammad & Ahmed, Rauf & Mehmood, Zafar. (2021). Deep Stacked Sparse Autoencoders -A Breast Cancer Classifier. Mehran University Research Journal of Engineering and Technology. Vol 41 No 1 (2022). 2413-7219. 10.22581/muet1982.2201.05.
Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000 Oct;16(10):906-14. doi: 10.1093/bioinformatics/16.10.906. PMID: 11120680.
Kashef, Amirarash & Khatibi, Toktam & (M.D, Azim. (2020). Treatment outcome classification of pediatric Acute Lymphoblastic Leukemia patients with clinical and medical data using machine learning: A case study at MAHAK hospital. Informatics in Medicine Unlocked. 20. 100399. 10.1016/j.imu.2020.100399.
Marzahl, C., Aubreville, M., Voigt, J., Maier, A. (2019). Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_2.
de Sant’Anna, Y.F.D.; de Oliveira, J.E.M.; Dantas, D.O. Interpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells. Computers 2022, 11, 125. https://doi.org/10.3390/computers11080125.
Ding, Yifan & Yang, Yujia & Cui, Yan. (2019). Deep Learning for Classifying of White Blood Cancer. 10.1007/978-981-15-0798-4_4. Gupta A, Gupta R, editors. ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Springer Singapore; 2019. p. 33–41.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2818-2826, doi: 10.1109/CVPR.2016.308.
G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 2261-2269, doi: 10.1109/CVPR.2017.243.
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231.
Safuan, Syadia & Tomari, Razali & Wan Zakaria, W.N & Mohd, Mohd & Hani Suriani, Nor Suraya. (2020). Investigation of white blood cell biomaker model for acute lymphoblastic leukemia detection based on convolutional neural network. Bulletin of Electrical Engineering and Informatics. 9. 10.11591/eei.v9i2.1857.
Raju, Anand & Shanthi, T. & Nithish, M. & Lakshman, S.. (2020). Face Recognition and Classification Using GoogleNET Architecture. 10.1007/978-981-15-0035-0_20.
“A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3”, WJCMS, vol. 2, no. 4, pp. 68–76, Dec. 2023, doi: 10.31185/wjcms.209.
Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019 Jul;75:34-46. doi: 10.1016/j.compmedimag.2019.05.001. Epub 2019 May 18. PMID: 31150950.
Salian, Saumya & Sawarkar, Dr. (2022). MELANOMA SKIN LESION CLASSIFICATION USING IMPROVED EFFICIENTNETB3. Jordanian Journal of Computers and Information Technology. 8. 1. 10.5455/jjcit.71-1636005929.
Kaiqiang Ma, Lingling Sun, Yaqi Wang, and Junchao Wang "Classification of blood cancer images using a convolutional neural networks ensemble", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117903 (14 August 2019); https://doi.org/10.1117/12.2539605.
Pan, Y., Liu, M., Xia, Y., & Shen, D. (2019). Neighborhood-correction algorithm for classification of normal and malignant cells. In Lecture Notes in Bioengineering (pp. 73-82). (Lecture Notes in Bioengineering). Springer. https://doi.org/10.1007/978-981-15-0798-4_8.
Verma, Ekansh & Singh, Vijendra. (2019). ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification. 10.1007/978-981-15-0798-4_14.
Prellberg, Jonas & Kramer, Oliver. (2019). Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks. 10.1007/978-981-15-0798-4_6.
Xiao, F., Kuang, R., Ou, Z., Xiong, B. (2019). DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_9.
Liu, Ying & Long, Feixiao. (2019). Acute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning. 10.1007/978-981-15-0798-4_12.
Ding, Y., Yang, Y., Cui, Y. (2019). Deep Learning for Classifying of White Blood Cancer. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_4.
Honnalgere, A., Nayak, G. (2019). Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images. In: Gupta, A., Gupta, R. (eds) ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0798-4_1.
P. Mathur, M. Piplani, R. Sawhney, A. Jindal and R. R. Shah, "Mixup Multi-Attention Multi-Tasking Model for Early-Stage Leukemia Identification," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1045-1049, doi: 10.1109/ICASSP40776.2020.9054672.
Gehlot S, Gupta A, Gupta R. SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. Med Image Anal. 2020 Apr;61:101661. doi: 10.1016/j.media.2020.101661. Epub 2020 Feb 4. PMID: 32066066.
Goswami, Shubham & Mehta, Suril & Sahrawat, Dhruva & Gupta, Anubha & Gupta, Ritu. (2020). Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer.
Lamberti, William. (2022). Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features.
Das, Pradeep & Nayak, Biswajit & Meher, Sukadev. (2022). A lightweight deep learning system for automatic detection of blood cancer. Measurement. 191. 110762. 10.1016/j.measurement.2022.110762.
Sulaiman, Adel & Anand, Vatsala & Gupta, Sheifali & Asiri, Yousef & Elmagzoub, M. & Al Reshan, Mana & Shaikh, Asadullah. (2023). A Convolutional Neural Network Architecture for Segmentation of Lung Diseases Using Chest X-ray Images. Diagnostics. 13. 1651. 10.3390/diagnostics13091651.
David A van Dyk & Xiao-Li Meng (2001) The Art of Data Augmentation, Journal of Computational and Graphical Statistics, 10:1, 1-50, DOI: 10.1198/10618600152418584.
Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., Mengko, T.R. (2019). Brain Tumor Classification Using Convolutional Neural Network. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/1. Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_33.
B. Zoph, V. Vasudevan, J. Shlens and Q. V. Le, "Learning Transferable Architectures for Scalable Image Recognition," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 8697-8710, doi: 10.1109/CVPR.2018.00907.
V. -T. Hoang and K. -H. Jo, "Practical Analysis on Architecture of EfficientNet," 2021 14th International Conference on Human System Interaction (HSI), Gdańsk, Poland, 2021, pp. 1-4, doi: 10.1109/HSI52170.2021.9538782.
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