Abstract

The immune system relies on white blood cells and platelets, which are both produced in the bone marrow and together account for around one percent of the blood corpuscles. Acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) are two major subtypes of acute leukaemia identifiable from its lineage. Unlike other chronic diseases, leukaemia is a curable blood disorder and patients’ survival is possible with precise treatment. The effectiveness of this disease's treatment can be greatly influenced by early diagnosis. This study focused on a deep neural network for the segmentation and classification of ALL using the SN-AM and ALL-IDB datasets and obtained from the cancer imaging archive (TCIA) repository. ResNet-50 and VGG-19, two of the most popular deep learning networks, were used. The use of stored weights was not used for these two networks; instead, we modified the weights and learning parameters. A UNet with InceptionV2 model was used for the segmentation, while convolutional neural network (CNN) was employed to train the images after feature selection. An improved CNN called i-Net with more convolutional layers and tuned hyperparameters was proposed for the classification into normal and cancerous white blood cells. Data augmentation, dropout regularization, and batch normalization were employed to reduce overfitting. ResNet-50, VGG-19, and a proposed deep neural network called “i-Net”, all have validation accuracy of 92.2%, 92.3%, and 99.18%, respectively. However, when trained without early stoppage, the model (i-Net) accuracy decreased after the 30th training cycle (epoch). CNN has shown to be accurate at diagnosing ALL according to this study. When compared to other pre-trained deep learning models such as the standard VGG-19 and ResNet-50, we achieved a better performance on the test dataset of about 630 microscopic images suggesting that the CNN can be used in clinical decision support systems (CDSS) for leukaemia detection.

Keyword

Acute lymphoblastic leukaemia, CNN, CDSS, Data augmentation, Deep learning, Image segmentation, Medical Imaging.

Cite this article

Ikechukwu AV, S. M

Refference

[1][1]Dong Y, Shi O, Zeng Q, Lu X, Wang W, Li Y, et al. Leukemia incidence trends at the global, regional, and national level between 1990 and 2017. Experimental Hematology & Oncology. 2020; 9(1):1-11.

[2][2]https://www.webmd.com/cancer/lymphoma/types-and-differences#1. Accessed 26 July 2022.

[3][3]Perkonigg M, Hofmanninger J, Menze B, Weber MA, Langs G. Detecting bone lesions in multiple myeloma patients using transfer learning. In data driven treatment response assessment and preterm, perinatal, and paediatric image analysis 2018 (pp. 22-30). Springer, Cham.

[4][4]Thanh TT, Vununu C, Atoev S, Lee SH, Kwon KR. Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering. 2018; 10(2):54-8.

[5][5]Kumar D, Jain N, Khurana A, Mittal S, Satapathy SC, Senkerik R, et al. Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access. 2020; 8:142521-31.

[6][6]Ianniciello A, Helgason GV. Autophagy in hematopoiesis and leukemogenesis. In autophagy in health and disease 2022 (pp. 125-41). Academic Press.

[7][7]Liu Y, Long F. Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning. In ISBI 2019 C-NMC challenge: classification in cancer cell imaging 2019 (pp. 113-21). Springer, Singapore.

[8][8]B KA, Kyjenko VM, Zukow W, Popovych IL. Causal neuro-immune relationships at patients with chronic pyelonephritis and cholecystitis. correlations between parameters EEG, HRV and white blood cell count. Open Medicine. 2017; 12(1):201-13.

[9][9]Fouladi S, Ebadi MJ, Safaei AA, Bajuri MY, Ahmadian A. Efficient deep neural networks for classification of COVID-19 based on CT images: virtualization via software defined radio. Computer Communications. 2021; 176:234-48.

[10][10]Kassani SH, Kassani PH, Wesolowski MJ, Schneider KA, Deters R. A hybrid deep learning architecture for leukemic B-lymphoblast classification. In 2019 international conference on information and communication technology convergence 2019 (pp. 271-6). IEEE.

[11][11]Xu L, Tetteh G, Lipkova J, Zhao Y, Li H, Christ P, et al. Automated whole-body bone lesion detection for multiple myeloma on 68Ga-pentixafor PET/CT imaging using deep learning methods. Contrast Media & Molecular Imaging. 2018.

[12][12]Zhao J, Zhang M, Zhou Z, Chu J, Cao F. Automatic detection and classification of leukocytes using convolutional neural networks. Medical & Biological Engineering & Computing. 2017; 55(8):1287-301.

[13][13]Abibullaev B, An J, Jin SH, Lee SH, Moon JI. Minimizing inter-subject variability in fNIRS-based brain–computer interfaces via multiple-kernel support vector learning. Medical Engineering & Physics. 2013; 35(12):1811-8.

[14][14]Mourya S, Kant S, Kumar P, Gupta A, Gupta R. LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer. arXiv preprint arXiv:1810.07961. 2018.

[15][15]He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In proceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-8).

[16][16]He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015; 37(9):1904-16.

[17][17]Labati RD, Piuri V, Scotti F. All-IDB: The acute lymphoblastic leukemia image database for image processing. In 2011 IEEE international conference on image processing 2011 (pp. 2045-8). IEEE.

[18][18]Duggal R, Gupta A, Gupta R, Mallick P. SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging. In international conference on medical image computing and computer-assisted intervention 2017 (pp. 435-43). Springer, Cham.

[19][19]Gupta A, Duggal R, Gehlot S, Gupta R, Mangal A, Kumar L, et al. GCTI-SN: geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. Medical Image Analysis. 2020.

[20][20]Duggal R, Gupta A, Gupta R, Wadhwa M, Ahuja C. Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In proceedings of the tenth Indian conference on computer vision, graphics and image processing 2016 (pp. 1-8).

[21][21]Gupta R, Mallick P, Duggal R, Gupta A, Sharma O. Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma. Clinical Lymphoma, Myeloma and Leukemia. 2017; 17(1).

[22][22]Foran DJ, Comaniciu D, Meer P, Goodell LA. Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy. IEEE Transactions on Information Technology in Biomedicine. 2000; 4(4):265-73.

[23][23]Rehman A, Abbas N, Saba T, Rahman SI, Mehmood Z, Kolivand H. Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique. 2018; 81(11):1310-7.

[24][24]Markiewicz T, Osowski S, Marianska B, Moszczynski L. Automatic recognition of the blood cells of myelogenous leukemia using SVM. In proceedings 2005 IEEE international joint conference on neural networks, 2005 (pp. 2496-501). IEEE.

[25][25]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.

[26][26]Abd HNH, Mashor MY, Hassan R. Automatic blasts counting for acute leukemia based on blood samples. International Journal of Research and Reviews in Computer Science. 2011; 2(4):971-6.

[27][27]Wu J, Zeng P, Zhou Y, Olivier C. A novel color image segmentation method and its application to white blood cell image analysis. In international conference on signal processing 2006.

[28][28]Salihah A, Nasir A, Mustafa N, Fazli N, Nasir M. Application of thresholding technique in determining ratio of blood cells for leukemia detection. Proceedings of the international conference on man-machine systems 2009 (pp.1-6).

[29][29]Horie Y, Yoshio T, Aoyama K, Yoshimizu S, Horiuchi Y, Ishiyama A, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointestinal Endoscopy. 2019; 89(1):25-32.

[30][30]Saba T, Khan MA, Rehman A, Marie-sainte SL. Region extraction and classification of skin cancer: aheterogeneous framework of deep CNN features fusion and reduction. Journal of Medical Systems. 2019; 43(9):1-19.

[31][31]Sekaran K, Chandana P, Krishna NM, Kadry S. Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer. Multimedia Tools and Applications. 2020; 79(15):10233-47.

[32][32]Zuluaga-gomez J, Al MZ, Benaggoune K, Meraghni S, Zerhouni N. A CNN-based methodology for breast cancer diagnosis using thermal images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2021; 9(2):131-45.

[33][33]Ikechukwu AV, Murali S, Deepu R, Shivamurthy RC. ResNet-50 vs VGG-19 vs training from scratch: a comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transitions Proceedings. 2021; 2(2):375-81.

[34][34]Dabeer S, Khan MM, Islam S. Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked. 2019.

[35][35]Ahmed N, Yigit A, Isik Z, Alpkocak A. Identification of leukemia subtypes from microscopic images using convolutional neural network. Diagnostics. 2019; 9(3):1-11.

[36][36]Genovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F. Histopathological transfer learning for acute lymphoblastic leukemia detection. In international conference on computational intelligence and virtual environments for measurement systems and applications 2021 (pp. 1-6). IEEE.

[37][37]Zolfaghari M, Sajedi H. A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells. Multimedia Tools and Applications. 2022; 81(5):6723-53.

[38][38]Vieira G, Valle ME. Acute lymphoblastic leukemia detection using hypercomplex-valued convolutional neural networks. arXiv preprint arXiv:2205.13273. 2022.

[39][39]Kasani PH, Park SW, Jang JW. An aggregated-based deep learning method for leukemic B-lymphoblast classification. Diagnostics. 2020; 10(12):1-17.

[40][40]Bodzas A, Kodytek P, Zidek J. Automated detection of acute lymphoblastic leukemia from microscopic images based on human visual perception. Frontiers in Bioengineering and Biotechnology. 2020.

[41][41]Alagu S. Automatic detection of acute lymphoblastic leukemia using UNET based segmentation and statistical analysis of fused deep features. Applied Artificial Intelligence. 2021; 35(15):1952-69.

[42][42]Jiang Z, Dong Z, Wang L, Jiang W. Method for diagnosis of acute lymphoblastic leukemia based on ViT-CNN ensemble model. Computational Intelligence and Neuroscience. 2021.

[43][43]Granero MA, Hernández CX, Valle ME. Quaternion-valued convolutional neural network applied for acute lymphoblastic leukemia diagnosis. In Brazilian conference on intelligent systems 2021 (pp. 280-93). Springer, Cham.

[44][44]Wang S, Xia X, Ye L, Yang B. Automatic detection and classification of steel surface defect using deep convolutional neural networks. Metals. 2021; 11(3):1-22.

[45][45]Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014.

[46][46]Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225. 2017.

[47][47]Loey M, Smarandache F, Khalifa NEM. Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry. 2020; 12(4):1-19.

[48][48]Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine. 2020; 43(2):635-40.

[49][49]Shafique S, Tehsin S, Anas S, Masud F. Computer-assisted acute lymphoblastic leukemia detection and diagnosis. In international conference on communication, computing and digital systems 2019 (pp. 184-9). IEEE.

[50][50]Mondal C, Hasan M, Jawad M, Dutta A, Islam M, Awal M, et al. Acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks. arXiv preprint arXiv:2105.03995. 2021.