An Efficient System for Detection and Classification of Acute Lymphoblastic Leukemia Using Semi-Supervised Segmentation Technique

Ratnamala Mantri; Rais Abdul Hamid Khan; Deepak T Mane

doi:10.54392/irjmt25210

Authors

Ratnamala Mantri School of Computer Science and Engineering, Sandip University, Nashik, India Author https://orcid.org/0009-0000-2398-3693
Rais Abdul Hamid Khan School of Computer Science and Engineering, Sandip University, Nashik, India Author
Deepak T Mane Department of Computer Science & Engineering, VIT, Pune, India. Author https://orcid.org/0000-0002-5387-150X

DOI:

https://doi.org/10.54392/irjmt25210

Keywords:

Acute lymphoblastic leukemia, CNN, Classification, Feature Extraction, K-means, Peripheral Blood Smear image, Semi-supervised Segmentation

Abstract

Acute lymphoblastic leukemia (ALL), sometimes referred to as hematopoietic cancer or blood cancer, is a group of cancers that impact lymphocytes, which are white blood cells. Improving patient outcomes and developing efficient treatment plans depend on early and precise blood cancer diagnosis. The lack of labelled data makes it difficult to segment lymphoblast cells from microscopic images. Our research aimed to achieve unsupervised approach for precise and accurate segmentation of blasted lymphocyte cells, thereby improving the overall performance of ALL detection and classification into its subtypes L1, L2 and L3. The proposed method employs k-means segmentation, where the parameter k is tuned, and optimal value is determined based on segmentation quality. For better performance, generated segments are evaluated against ground truth image based on Structural Similarity Index Measure (SSIM), Dice similarity coefficient (DSC) and Intersection over union (IoU). The algorithm iterates over different values of k, assesses the segmentation quality, and selects the segment with the highest evaluation score. Customized convolutional neural networks are employed for categorization. The data augmentation technique has been applied to expand the amount of training data in order to enhance model efficiency. The ALL-IDB dataset is used to assess the model's performance, and the experimental results showed that the suggested method can identify blasted cell subtypes with an overall accuracy of 99%. We succeeded in detecting acute lymphoblastic leukemia with 100% accuracy. Our proposed model not only enhances the accuracy significantly but also determines the optimal value of clusters (k) for more effective segmentation.

References

R. Asghar, S. Kumar, A. Shaukat, P. Hynds, Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. Plos one, 19(6), (2024) e0292026. https://doi.org/10.1371/journal.pone.0292026

S. Khan, M. Sajjad, T. Hussain, A. Ullah, A. S. Imran, A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. Ieee Access, 9, (2020)10657-10673. https://doi.org/10.1109/ACCESS.2020.3048172

I.A. Ahmed, E.M. Senan, H.S.A. Shatnawi, Z.M. Alkhraisha, M.M.A. Al-Azzam, Hybrid techniques for the diagnosis of acute lymphoblastic leukemia based on fusion of CNN features. Diagnostics, 13(6), (2023) 1026. https://doi.org/10.3390/diagnostics13061026

S. Mohapatra, D. Patra, S. Satpathy, An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Computing and Applications, 24, (2014) 1887-1904. https://doi.org/10.1007/s00521-013-1438-3

R.D. Labati, V. Piuri, F. Scotti, (2011) All-IDB: The acute lymphoblastic leukemia image database for image processing. In 2011 18th IEEE international conference on image processing, IEEE, Belgium. https://doi.org/10.1109/ICIP.2011.6115881

K. Sathishkumar, M. Chaturvedi, P. Das, S. Stephen, P. Mathur, Cancer incidence estimates for 2022 & projection for 2025: result from National Cancer Registry Programme, India. Indian journal of medical research, 156(4&5), (2022) 598-607. https://doi.org/10.4103/ijmr.ijmr_1821_22

T. Saba, Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges. Journal of infection and public health, 13(9), (2020) 1274-1289. https://doi.org/10.1016/j.jiph.2020.06.033

H.E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, T. Ganslandt, Transfer learning for medical image classification: A literature review. BMC medical imaging, 22(1), (2022) 69. https://doi.org/10.1186/s12880-022-00793-7

R. Mantri, R.A.H. Khan, S Jadhav, Leukemia Diagnosis using Transfer Learning: An Efficient Approach. Frontiers in Health Informatics, 13(2), (2024) 481-496. https://doi.org/10.52783/fhi.vi.38

A. Girdhar, H. Kapur, V. Kumar, Classification of white blood cell using convolution neural network. Biomedical Signal Processing and Control, 71, (2022) 103156. https://doi.org/10.1016/j.bspc.2021.103156

M.F.Talaat, S.A. Gamel, Machine learning in detection and classification of leukemia using C-NMC_Leukemia. Multimedia Tools and Applications, 83(3), (2024) 8063-8076. https://doi.org/10.1007/s11042-023-15923-8

A. Sulaiman, S. Kaur, S. Gupta, H. Alshahrani, M.S.A. Reshan, S. Alyami, A. Shaikh, ResRandSVM: Hybrid approach for acute lymphocytic leukemia classification in blood smear images. Diagnostics, 13(12), (2023) 2121. https://doi.org/10.3390/diagnostics13122121

A. Gupta, R. Gupta, (2019) ALL Challenge Dataset of ISBI 2019 [Data Set], Cancer Imaging Archive. https://www.cancerimagingarchive.net/collection/c-nmc-2019/

S. Shambhu, D. Koundal, P. Das, V.T. Hoang, K. Tran-Trung, H. Turabieh, Computational methods for automated analysis of malaria parasite using blood smear images: recent advances. Computational intelligence and neuroscience, 2022(1), (2022) 3626726. https://doi.org/10.1155/2022/3626726

A. Loddo, C. Di Ruberto, M. Kocher, G. Prod’Hom, MP-IDB: the malaria parasite image database for image processing and analysis. Processing and Analysis of Biomedical Information: First International SIPAIM Workshop, Lecture Notes in Computer Science, 11379, (2019) 57-65. https://doi.org/10.1007/978-3-030-13835-6_7

R.B. Hegde, K. Prasad, H. Hebbar, I. Sandhya, Peripheral blood smear analysis using image processing approach for diagnostic purposes:A review. Biocybernetics and Biomedical Engineering, 38(3), (2018) 467–480. https://doi.org/10.1016/j.bbe.2018.03.002

A.R. Andrade, L.H. Vogado, R. de MS Veras, R.R. Silva, F.H. Araujo, F.N. Medeiros, Recent computational methods for white blood cell nuclei segmentation: A comparative study. Computer methods and programs in biomedicine, 173, (2019) 1-14. https://doi.org/10.1016/j.cmpb.2019.03.001

L. Putzu, G. Caocci, C. Di Ruberto, Leucocyte classification for leukaemia detection using image processing techniques. Artificial intelligence in medicine, 62(3), (2014) 179-191. https://doi.org/10.1016/j.artmed.2014.09.002

P.P. Banik, R. Saha, K.D. Kim, An automatic nucleus segmentation and CNN model based classification method of white blood cell. Expert Systems with Applications, 149, (2020) 113211. https://doi.org/10.1016/j.eswa.2020.113211

S. Shafique, S. Tehsin, Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in cancer research & treatment, 17, (2018). https://doi.org/10.1177/1533033818802789

A. Bouchet, S. Montes, V. Ballarin, I. Díaz, Intuitionistic fuzzy set and fuzzy mathematical morphology applied to color leukocytes segmentation. Signal, Image and Video Processing, 14, (2020) 557-564. https://doi.org/10.1007/s11760-019-01586-2

R. Khandekar, P. Shastry, S. Jaishankar, O. Faust, N. Sampathila, Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis. Biomedical Signal Processing and Control, 68, (2021) 102690. https://doi.org/10.1016/j.bspc.2021.102690

S. Saleem, J. Amin, M. Sharif, M.A. Anjum, M. Iqbal, S.H. Wang, A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models, Complex & Intelligent Systems, 8, (2021) 3105–3120. https://doi.org/10.1007/s40747-021-00473-z

A.H. Alharbi, C.V. Aravinda, M. Lin, P.S. Venugopala, P. Reddicherla, M.A. Shah, Segmentation and classification of white blood cells using the UNet. Contrast Media & Molecular Imaging, 2022(1), (2022) 5913905. https://doi.org/10.1155/2022/5913905

S. Kadry, V. Rajinikanth, D. Taniar, R. Damaševičius, X.P.B. Valencia, Automated segmentation of leukocyte from hematological images—a study using various CNN schemes, The Journal of Supercomputing, 78(5), (2022) 6974-6994. https://doi.org/10.1007/s11227-021-04125-4

A.R. Revanda, C. Fatichah, N. Suciati, Classification of Acute Lymphoblastic Leukemia on White Blood Cell Microscopy Images Based on Instance Segmentation Using Mask R-CNN. International Journal of Intelligent Engineering & Systems, 15(5), (2022) 625-637. https://doi.org/10.22266/ijies2022.1031.54

E. Chen, R. Liao, M.Y. Shalaginov, T.H. Zeng, (2022) Real-time detection of acute lymphoblastic leukemia cells using deep learning, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, USA. https://doi.org/10.1109/BIBM55620.2022.9995131

P.K. Das, B. Sahoo, S. Meher, An efficient detection and classification of acute leukemia using transfer learning and orthogonal softmax layer-based model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(3), (2022) 1817-1828. https://doi.org/10.1109/TCBB.2022.3218590

T.G. Devi, N. Patil, S. Rai, C.S. Philipose, Gaussian Blurring Technique for Detecting and Classifying Acute Lymphoblastic Leukemia Cancer Cells from Microscopic Biopsy Images. Life, 13(2), (2023) 348. https://doi.org/10.3390/life13020348

S.I. Ur Rahman, M. Jadoon, S. Ali, H. Khattak, J. Huang, Efficient segmentation of lymphoblast in acute lymphocytic leukemia. Scientific Programming, 2021(1), (2021) 7488025. https://doi.org/10.1155/2021/7488025

B.S.S. Rao, B.S. Rao, An effective WBC segmentation and classification using MobilenetV3–ShufflenetV2 based deep learning framework. IEEE Access, 11, (2023) 27739-27748. https://doi.org/10.1109/ACCESS.2023.3259100

M. MoradiAmin, M. Yousefpour, N. Samadzadehaghdam, L. Ghahari, M. Ghorbani, M. Mafi, Automatic classification of acute lymphoblastic leukemia cells and lymphocyte subtypes based on a novel convolutional neural network. Microscopy Research and Technique, 87(7), (2024) 1615-1626. https://doi.org/10.1002/jemt.24551

D. Müller, I. Soto-Rey, F. Kramer, Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15(1), (2022) 210. https://doi.org/10.1186/s13104-022-06096-y

A.K. Venkataramanan, C. Wu, A.C. Bovik, I. Katsavounidis, Z. Shahid, A hitchhiker’s guide to structural similarity. IEEE Access, 9, (2021) 28872-28896. https://doi.org/10.1109/ACCESS.2021.3056504

P.K. Das, P. Jadoun, S. Meher, (2020) Detection and classification of acute lymphocytic leukemia. In 2020 IEEE-HYDCON, IEEE, India. https://doi.org/10.1109/HYDCON48903.2020.9242745

H.A. Abed, A. Mohammed, Proposing an efficient CNN model for detection of acute lymphoblastic leukemia (ALL) using transfer learning. International Journal of Advance Computational Engineering and Networking (IJACEN), 10(8), (2022) 53.

A.K. Al-Bashir, R.E. Khnouf, L.R. Bany Issa, Leukemia classification using different CNN-based algorithms-comparative study. Neural Computing and Applications, 36(16), (2024) 9313-9328. https://doi.org/10.1007/s00521-024-09554-9