Increment Learning for Acute Lymphoblastic Leukemia Classification

Increment Learning for Acute Lymphoblastic Leukemia Classification
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : G. Mercy Bai, P. Venkadesh
DOI : 10.14445/22315381/IJETT-V70I11P240

How to Cite?

G. Mercy Bai, P. Venkadesh, "Increment Learning for Acute Lymphoblastic Leukemia Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 393-409, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P240

Abstract
Acute Leukemia is a dangerous blood disease originating from bone marrow, that is commonly seen both in adults and children. Acute Lymphoblastic Leukemia (ALL) is a prominent one in Leukemia disease as it resists the body to fight pathogens and spread rapidly throughout the body. ALL is identified manually from bone marrow examination and blood smear but, this manual diagnosis of ALL is a slow process and is inaccurate which tends to immediate death. Moreover, various other techniques are also carried out for diagnosis that is costlier and suffer from catastrophic forgetting when learning new classes incrementally. To rectify these issues, an effectual strategy is developed for incremental classification of ALL. Here, the classification is done by a Deep Convolutional Neural Network (Deep CNN) trained by the Fractional Horse Whale Optimization Algorithm (Fractional-HWOA), which is the integration of the Fractional concept into the Horse Herd Optimization Algorithm (HOA) and Whale Optimization Algorithm (WOA) respectively. Various stages included in this paper are pre-processing, segmenting, extracting features, and classifying the image. Here, the Gaussian filter is used for pre-processing and Generative Adversarial Network (GAN) perform the process of segmentation. Alternatively, incremental classification is accomplished using Deep CNN where the network classifier is trained using the proposed Fractional-HWOA. Finally, weights are bounded by the entropy function based on an error condition. This Proposed method is evaluated using various parameters such as accuracy, sensitivity, and specificity and the values attained are 0.961, 0.957, and 0.958, accordingly

Keywords
Increment Learning, Acute Lymphoblastic Leukemia, Whale Optimization Algorithm, Deep Convolutional Neural Network, Horse Herd Optimization.

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