Implementation for Detection and Classification of Leukemia Disease using DCNN Algorithm

Authors

  • Dhuli Chaithanya  PG Scholar, Department of ECE, Siddartha Educational Academy Group of Institutions, C. Gollapalli, Andhra Pradesh, India.
  • D. Vijaya Kumar Reddy  Assistant professor, Department of ECE, Siddartha Educational Academy Group of Institutions, C. Gollapalli, Andhra Pradesh, India

Keywords:

Pre-processing, Adaptive Histogram analysis, MATLAB 2013a Version, R-Plane, B-Plane Separation, Multithreshold Algorithm, Pixel Averaging

Abstract

Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. Deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, deployed pre-trained Online AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. For acute lymphoblastic leukemia detection, achieved an acute lymphoblastic leukemia subtype classification the Accuracy was 96%, and specificity was 92.85%, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

References

  1. Kumar, S.; Mishra, S.; Asthana, P. Automated detection of acute leukemia using k-mean clustering algorithm. In Advances in Computer and Computational Sciences; Springer: Berlin/Heidelberg, Germany, 2018; pp. 655–670.
  2. Classification of Blasts in Acute Leukemia Blood samples Using k-Nearest Neighbour—IEEE Conference Publication. Available online: https://ieeexplore.ieee.org/abstract/document/6194769/ (accessed on 3 February 2020).
  3. Madhukar, M.; Agaian, S.; Chronopoulos, A.T. Deterministic model for acute myelogenous leukemia classification. In Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, Korea, 14–17 October 2012; pp. 433–438.
  4. Setiawan, A.; Harjoko, A.; Ratnaningsih, T.; Suryani, E.; Palgunadi, S. Classification of cell types in Acute Myeloid Leukemia (AML) of M4, M5 and M7 subtypes with support vector machine classifier. In Proceedings of the 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 6–7 March 2018; pp. 45–49.
  5. Faivdullah, L.; Azahar, F.; Htike, Z.Z.; Naing, W.N. Leukemia detection from blood smears. J. Med. Bioeng. 2015, 4, 488–491. [CrossRef]
  6. Laosai, J.; Chamnongthai, K. Acute leukemia classification by using SVM and K-Means clustering. In Proceedings of the 2014 IEEE International Electrical Engineering Congress (iEECON), Chonburi, Thailand, 19–21 March 2014; pp. 1–4.
  7. Patel, N.; Mishra, A. Automated leukaemia detection using microscopic images. Procedia Comput. Sci. 2015, 58, 635–642. [CrossRef]
  8. Sajjad, M.; Khan, S.; Jan, Z.; Muhammad, K.; Moon, H.; Kwak, J.T.; Rho, S.; Baik, S.W.; Mehmood, I. Leukocytes classification and segmentation in microscopic blood smear: A resource-aware healthcare service in smart cities. IEEE Access 2016, 5, 3475–3489. [CrossRef]
  9. Abdeldaim, A.M.; Sahlol, A.T.; Elhoseny, M.; Hassanien, A.E. Computer-aided acute lymphoblastic leukemia diagnosis system based on image analysis. In Advances in Soft Computing and Machine Learning in Image Processing; Springer: Berlin/Heidelberg, Germany, 2018; pp. 131–147.
  10. Dwivedi, A.K. Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput. Appl. 2018, 29, 1545–1554. [CrossRef].
  11. Sahlol, A.T.; Abdeldaim, A.M.; Hassanien, A.E. Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm. Soft Comput. 2019, 23, 6345–6360. [CrossRef]
  12. Dharani, N. P., and N. Gireesh. "Fusion of CT and PET Image of Lungs Using Hybrid Algorithms." Solid State Technology 63.6 (2020): 7706-7719.
  13. F. Scotti, "Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images", in Proc. of the 2006 IEEE Instrumentation and Measurement Technology Conf. (IMTC 2006), Sorrento, Italy, pp. 43-48, April 24-27, 2006. ISSN: 1091-5281. [DOI:10.1109/IMTC.2006.328170]
  14. Dharani, N. P., and Polaiah Bojja. "Analysis and prediction of COVID-19 by using recurrent LSTM neural network model in machine learning." International Journal of Advanced Computer Science and Applications 13.5 (2022).
  15. Dharani, N. P. "Detection of breast cancer by thermal based sensors using multilayered neural network classifier." International Journal of Engineering and Advanced Technology. (2019).

International Journal of Scientific Research in Science and Technology

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Published

2023-08-30

Issue

Volume 10, Issue 4, July-August-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dhuli Chaithanya, D. Vijaya Kumar Reddy "Implementation for Detection and Classification of Leukemia Disease using DCNN Algorithm" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 4, pp.178-189, July-August-2023.