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Segmentation and classification of white blood cancer cells from bone marrow microscopic images using duplet-convolutional neural network design

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Abstract

Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. The introduction of image processing techniques and computer-aided diagnostics is beneficial to this field as the results obtained by utilizing these methods are quick and accurate. The proposed method in this paper uses a design Convolutional Leaky RELU with CatBoost and XGBoost (CLR-CXG) to segment the images and extract the important features that help in classification. The binary classification algorithm and gradient boosting algorithm CatBoost (Categorical Boost) and XGBoost (Extreme Gradient Boost) are implemented individually. Moreover, Convolutional Leaky RELU with CatBoost (CLRC) is designed to decrease bias and provide high accuracy, while Convolutional Leaky RELU with XGBoost (CLRXG) is designed for classification or regression prediction problems which will increase the speed of executing the algorithm and improve its performance. Thus the CLR-CXG classifies the test images into Acute Lymphoblastic Leukemia (ALL) or Multiple Myeloma (MM). Finally, the CLRC algorithm achieved 100% accuracy in classifying cancer cells, and the recorded run time is 10s. Moreover, the CLRXG algorithm has gained an accuracy of 97.12% for classifying cancer cells and 12 s for executing the process.

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Data availability

The datasets generated during and/or analysed during the current study are available in the “The Cancer Imaging Archive” repository, following is the link to browse and download the dataset https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=52757009

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Mangalore, Karnataka, India

    Tulasi Gayatri Devi & Nagamma Patil

  2. Department of Pathology, Kasturba Medical College, Mangalore, Karnataka, India

    Sharada Rai & Cheryl Philipose Sarah

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  1. Tulasi Gayatri Devi
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Correspondence to Tulasi Gayatri Devi.

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Devi, T.G., Patil, N., Rai, S. et al. Segmentation and classification of white blood cancer cells from bone marrow microscopic images using duplet-convolutional neural network design. Multimed Tools Appl 82, 35277–35299 (2023). https://doi.org/10.1007/s11042-023-14899-9

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