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DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

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Abstract

Acute Lymphoblastic Leukemia is one of the fatal types of disease which causes a high mortality rate among children and adults. Traditional diagnosing of this disease is achieved through analyzing the microscopic images of white blood cells by a clinical pathologist. However, this procedure relies on manual observation and often provides inaccurate results. This research proposes an automated system for diagnosing Acute Lymphoblastic Leukemia disease using a convolutional neural network technique. For this purpose, simulation work has been performed over the Acute Lymphoblastic Leukemia-IDB 1 and Leukemia-lDB 2 datasets. However, data augmentation techniques have been employed to generate images to handle the overfitting problem in the model. Qualitative analysis has been performed by visualizing the intermediate layer activation, ConvNet filters and heatmap layers, and a comparative study has been performed with existing methods to validate the efficiency of our proposed model. However, the results showed that our proposed model attained 99.61% accuracy in Acute Lymphoblastic Leukemia diagnosis. The high accuracy reveals that it provides a more effective way to detect Acute Lymphoblastic Leukemia disease than existing works reported in the same area.

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Authors and Affiliations

  1. Mixit Inc. ION Group of Companies, Karachi, Pakistan

    Umair Saeed

  2. Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan

    Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Aftab Ahmed Shaikh & Athaul Rai

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  1. Umair Saeed
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Correspondence to Asif Ali Laghari.

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Appendix 1 Pseudo Code of Proposed Architecture

Appendix 1 Pseudo Code of Proposed Architecture

Table 5

Table 5 Algo 1: Pseudo Code Proposed Architecture
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Saeed, U., Kumar, K., Khuhro, M.A. et al. DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification. Multimed Tools Appl 83, 21019–21043 (2024). https://doi.org/10.1007/s11042-023-16191-2

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