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ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia

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Abstract

Leukaemia image classification involves using machine learning, and often deep learning, techniques to automatically analyse medical images and categorize them into different classes, such as different types of leukaemia or distinguishing between leukaemia and normal cells. Leukaemia, a kind of cancer that may occur in individuals of all ages, including kids and adults, is a significant contributor to worldwide death rates. Additionally, inspection under a microscope, leukemic cells look and develop similarly to normal cells, making identification more difficult. Traditional machine learning models may struggle to scale effectively with the increasing size and complexity of medical image datasets. As the datasets grow, the limitations of manually engineered features and shallow architectures become more apparent. In the context of leukaemia image classification using deep learning, the primary aim is to develop a robust and accurate model that can effectively distinguish between different types of leukaemia or between leukaemia and normal cells within medical images. As a consequence, the study provided a unique deep learning approach known as optimized deep recurrent neural network (ODRNN) for identifying leukaemia sickness by analysing microscopic images of blood samples. Deep recurrent neural networks (DRNNs) are used in the recommended strategy for diagnosing leukaemia, and then, the red deer optimization algorithm (RDOA) applies to optimize the weight gained by DRNN. The weight of DRNN from RDOA will be tuned on the deer roaring rate behaviour. The model that has been proposed is evaluated on two openly accessible leukaemia blood sample datasets, AML, ALL_IDB1, and ALL_IDB2. The research work uses statistical metrics related to disease including specificity, recall, accuracy, precision, and F1-score to assess the effectiveness of the proposed model for identification and classification. The proposed method achieves highly impressive results, with scores of 98.96%, 99.85%, 99.98%, 99.23%, and 99.98%, respectively. In conclusion, deep learning-based image classification for leukaemia has emerged as a powerful and promising approach in the field of medical diagnostics. Leveraging ODRNN, researchers and practitioners have made significant strides in automating the process of identifying and classifying leukaemia cells in medical images.

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Data availability

The datasets generated during and/or analysed during the current study are available in the UCI repository.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India

    K. Dhana Shree

  2. Department of Information Technology, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

    S. Logeswari

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  1. K. Dhana Shree
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  2. S. Logeswari
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Contributions

KDS contributed to conceptualization, data curation, formal analysis, methodology, writing–original draft, and writing–review and editing. SL was involved in investigation, formal analysis, and supervision.

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Correspondence to K. Dhana Shree.

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Shree, K.D., Logeswari, S. ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia. SIViP (2024). https://doi.org/10.1007/s11760-024-03062-y

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