Integration of Convolutional Features and Residual Neural Network for the Detection and Classification of Leukemia from Blood Smear Images

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : V. Shalini, K. S. Angel Viji
DOI : 10.14445/22315381/IJETT-V70I9P218

How to Cite?

V. Shalini, K. S. Angel Viji, "Integration of Convolutional Features and Residual Neural Network for the Detection and Classification of Leukemia from Blood Smear Images" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 176-184, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P218

Abstract
Malignant Acute Lymphoblastic Leukemia (ALL) attacks blood and bone marrow. Leukemia mostly affects both youngsters and older people all over the world. It is critical to detect leukaemia slightly earlier to offer patients the best possible therapy, specifically in the case of youngsters. As a result, computational methods for medical image processing are in high demand and have been the focus of medical image processing research. The major goal of this study is to use image processing and artificial intelligence approaches to forecast ALL cells. Computer Aided Diagnosis (CAD) has quickly grown in popularity over the last few years. Early-stage leukemia detection that is swift, safe, and precise is critical for treating and preserving patients' lives. A residual Neural Network (RNN) can be identified as a variant of a neural network that is again modified as ResNet-50. The max-pooling and convolutional layers were utilized to extract the maximum features present in the source image. The completely linked layer differentiates between malignant and healthy cell images. ResNet50 was used to detect leukemic cells with 99.61% accuracy. The findings showed that the proposed model outperformed other famous algorithms in detecting healthy vs leukemia patients.

Keywords
Acute lymphoblastic leukemia, Computer-aided diagnosis, Convolutional layer, Medical image processing, ResNet-50.

Reference
[1] The Leukemia & Lymphoma Society, New York. Https://Www.Ils.Org/Facts-and-Statistics [Accessed 16 November 2019].
[2] Cancer Research UK. Http://Www.Cancerresearchuk.Org [Accessed 16 November 2019].
[3] Anilkumar, K. K., V. J. Manoj, and T. M. Sagi. "A Survey on Image Segmentation of Blood and Bone Marrow Smear Images With Emphasis To Automated Detection of Leukemia," Biocybernetics and Biomedical Engineering , vol.40, no.4, pp. 1406-1420, 2020.
[4] Henry JB. Clinical Diagnosis and Management by Laboratory Methods. 17th Ed. Philadelphia: W.B. Saunders Company, 1989
[5] Dese, Kokeb, Hakkins Raj, Gelan Ayana, Tilahun Yemane, Wondimagegn Adissu, Janarthanan Krishnamoorthy, and Timothy Kwa, "Accurate Machine-Learning-Based Classification of Leukemia From Blood Smear Images," Clinical Lymphoma Myeloma and Leukemia, vol.21, no.11, pp. E903-E914, 2021.
[6] Sahlol AT, Abdeldaim AM, Hassanien AE, “ Automatic Acute Lymphoblastic Leukemia Classification Model Using Social Spider Optimization Algorithm,” Soft Comput, vol.5, pp.1–16, 2019.
[7] Abhishek, A., Jha, R. K., Sinha, R., & Jha, K, “Automated Classification of Acute Leukemia on A Heterogeneous Dataset Using Machine Learning and Deep Learning Techniques,” Biomedical Signal Processing and Control, vol.72, No.10, pp. 33-41, 2022.
[8] Shafique, S., & Tehsin, S, “Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks,” Technology in Cancer Research & Treatment, vol.17, 2018
[9] Gargi Sharma, Gourav Shrivastava, "Crop Disease Prediction Using Deep Learning Techniques - A Review," SSRG International Journal of Computer Science and Engineering, vol.9, no.4, pp. 23-28, 2022. Crossref, Https://Doi.Org/10.14445/23488387/IJCSEV9I4P104
[10] Reta. Carolina, Altamirano. Leopoldo, A. Gonzalez. Jesus, Hernandez. Raquel, Diaz, Peregrina. Hayde, Olmos. Ivan, E. Alonso. Jose, and Lobato. Ruben, “ Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias,” PLOS ONE, vol.10, No.6, 2015.
[11] Fatichah. Chastine, L. Tangel. Martin, Yan. Fei, P. Betancourt. Janet, M. Rahmat, Widyanto, Dong. Fangyan, and Hirota. Kaoru, “Fuzzy Feature Representation for White Blood Cell Differential Counting in Acute Leukemia Diagnosis,” International Journal of Control, Automation, and Systems, vol.13, No.3, pp.742-752, 2015.
[12] Tosta. Thana, A. Azevedo, Faria. Paulo, Rogrio, Batista. Valrio, Ramos, Neves. Leandro, Alves, and Do Nascimento. Marcelo, Zanchetta, “ Using Wavelet Sub-Band and Fuzzy 2-Partition Entropy To Segment Chronic Lymphocytic Leukemia Images, “ Applied Soft Computing, vol.6, No.4, pp.49-58, 2018.
[13] Zhijie Yang, Hongbin Huang, "Garbage Classification Based on Deep Residual Weakly Supervised Learning Model," International Journal of Recent Engineering Science, vol.7, No.3, pp.47-51, 2020.
[14] Zhao, J., Zhang, M., Zhou, Z., Chu, J., & Cao, F, “Automatic Detection and Classification of Leukocytes Using Convolutional Neural Networks,” Medical & Biological Engineering & Computing, vol.55, No.8, pp.1287-1301, 2017.
[15] F. Wang, L. P. Casalino, and D. Khullar, “Deep Learning in Medicine-Promise, Progress, and Challenges,” JAMA Internal Medicine, vol.179, no.3, pp. 293-294, 2019.
[16] F. Wang, L. P. Casalino, and D. Khullar, “Deep Learning in Medicine-Promise, Progress, and Challenges,” JAMA Internal Medicine, vol.179, no.3, pp. 293-294, 2019.
[17] Rawat, Jyoti, Annapurna Singh, H. S. Bhadauria, and Jitendra Virmani,"Computer Aided Diagnostic System for Detection of Leukemia Using Microscopic Images ," Procedia Computer Science , vol.70 , pp.748-756, 2015.
[18] Al-Hafiz, Fatimah, Shiroq Al-Megren, and Heba Kurdi, "Red Blood Cell Segmentation By Thresholding and Canny Detector," Procedia Computer Science, vol.141, pp.327-334, 2018.
[19] Rezatofighi, S. H., R. A. Zoroofi, R. Sharifian, and H. Soltanian-Zadeh, "Segmentation of Nucleus and Cytoplasm of White Blood Cells Using Gram-Schmidt Orthogonalization and Deformable Models," in 2008 9th International Conference on Signal Processing, IEEE, pp. 801-805, 2008.
[20] S Prathiba, Dr. B. Sivagami, "Newfangled Applications of Digital Image Processing," SSRG International Journal of Computer Science and Engineering, vol.6, no.11, pp. 28-32, 2019. Crossref, Https://Doi.Org/10.14445/23488387/IJCSE-V6I11P106
[21] Nageswari P, Rajan S, Manivel K, “Medical Image Segmentation Approaches: A Survey,” SSRG International Journal of Electronics and Communication Engineering, vol.7, no.7, pp. 1-3, 2020. Crossref, Https://Doi.Org/10.14445/23488549/IJECE-V7I7P101
[22] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 2016.
[23] S. Mohapatra and D. Patra, “Automated Cell Nucleus Segmentation and Acute Leukemia Detection in Blood Microscopic Images,” in Proceedings of The 2010 International Conference on Systems in Medicine and Biology, December 2010.
[24] Bindhu, Dr. K. K. Thanammal, "Analytical Study on Digital Image Processing Applications," SSRG International Journal of Computer Science and Engineering, vol.7, no.6, pp. 4-7, 2020. Crossref, Https://Doi.Org/10.14445/23488387/IJCSE-V7I6P102
[25] A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” New England Journal of Medicine, vol.380, no.14, pp. 1347– 1358, 2019.
[26] Abhir Bhandary, Ananth Prabhu G, Mustafa Basthikodi, Chaitra K M ,” Early Diagnosis of Lung Cancer Using Computer Aided Detection Via Lung Segmentation Approach,” International Journal of Engineering Trends and Technology, vol.69, No.5, pp.85-93, 2021.
[27] Pirouzbakht, Natalia, and J. Mejía, "Algorithm for the Detection of Breast Cancer in Digital Mammograms Using Deep Learning," RCCS+ SPIDTEC2 , 2017.
[28] R. D. Labati, V. Piuri, and F. Scotti, “ALL-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing,” 2011.
[29] Ebanesar.C, Hamsa Vagini R, Sangeetha Bregit.A, Dr. J Dinesh Peter, “Computer Aided System for Automated Heterogeneous Cancer Recognition Using Google Cloud Platform,” SSRG International Journal of Computer Science and Engineering, vol.5, no.5, pp. 11-16, 2018. Crossref, Https://Doi.Org/10.14445/23488387/IJCSE-V5I5P103
[30] S Vogado. Luis, H, S Veras. Rodrigo, M, D Araujo. Flavio, H, V Silva. Romuere, R, and T Aires. Kelson, R, “Leukemia Diagnosis in Blood Slides Using Transfer Learning in Cnns and SVM for Classification,” Engineering Applications of Artificial Intelligence, vol.2, pp.415-422, 2018.