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Classification of acute lymphoblastic leukemia using improved ANFIS

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Abstract

Many advanced technologies have been developed in the medical field where leukaemia plays a vital role, which may cause serious issues when it is unidentified. In the convolution method, human error may occur, so to avoid it, many tools have been introduced, like Adaptive Network-Based Fuzzy Inference Systems (ANFIS), which helps to diagnose and classify systems for leukaemia, and it is also shown to be an excellent function approximation tool. ANFIS also uses the ANN theory, which is used to conclude the attributes of neuro-fuzzy systems. But the accuracy is not up to the mark. To overcome this drawback, we have proposed an improved ANFIS (I ANFIS) model to predict leukaemia data using a Euclidean distance to measure between the trained feature data and the test feature data. An Improved Adaptive Neuro-Fuzzy Neural Network (ANFNN) is also introduced, which helps the input space be partitioned into many local regions by the fuzzy clustering, in which the computation complexity is decreased and, based on both the separation and the compactness among the clusters, the fuzzy rule number is determined by the validity function. Then, the premise parameters and consequent parameters are trained by a hybrid learning algorithm which uses forward and backward passes. Following the arrangement of principle parameters, until layer 4, a node outputs move ahead in the forward pass and, using Least Square Estimate (LSE), the consequent parameters are calculated for each node. Then an error measure is calculated for each node. To update principal parameters, the error signals are distributed backward with gradient descent in the backward pass. Improved ANFIS obtains the best accuracy, sensitivity, specificity of 97.14%, 96% and 90%, and classification for all the cell types, especially in the microscopic blood cell dataset.

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Data availability

Data Citation: Mehrad Aria, Mustafa Ghaderzadeh, Davood Bashash, Hassan Abolghasemi, Farkhondeh Asadi, and Azamossadat Hosseini, “Acute Lymphoblastic Leukemia (ALL) image dataset.” Kaggle, (2021). DOI: https://doi.org/10.34740/KAGGLE/DSV/2175623.

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Funding

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Authors and Affiliations

  1. Scott Christian College (Autonomous), Nagercoil, Tamil Nadu, 629003, India

    M. Anline Rejula

  2. Vellore Institute of Technology, Chennai, India

    S. Amutha

  3. Noorul Islam College of Arts and Science, Kumaracoil, 629180, India

    G. M. Shilpa

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  1. M. Anline Rejula
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  2. S. Amutha
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  3. G. M. Shilpa
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Rejula, M.A., Amutha, S. & Shilpa, G.M. Classification of acute lymphoblastic leukemia using improved ANFIS. Multimed Tools Appl 82, 35475–35491 (2023). https://doi.org/10.1007/s11042-023-15113-6

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