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Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

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Abstract

Medical imaging plays an essential role in modern healthcare, helping accurate diagnoses and effective treatment strategies. Still, the quality and interpretability of medical images are regularly hindered by various sources of noise. This paper presents a comprehensive exploration of traditional noise reduction techniques in medical imaging, addressing challenges posed by quantum noise, electronic noise, radiation interference, and other factors. The study delves into spatial filtering, frequency domain filtering, statistical methods, probability-based noise reduction, and adaptive filtering techniques. Each method is analyzed for its applicability and effectiveness in mitigating noise while preserving diagnostically relevant information. The comparative analysis provides insights into the strengths and limitations of these techniques, guiding practitioners in selecting appropriate methods based on imaging modalities and noise characteristics. Also, the paper highlights future research directions, emphasizing the potential of advanced Machine Learning (ML) models and the integration of multimodal data for enhanced noise removal.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. National Institute of Technology, Patna, Bihar, 800005, India

    Ravi Ranjan Kumar

  2. Faculty of Engineering and Technology, ITER, Siksha ‘O’ Anusandhan (Deemed to Be University), Bhubaneswar, 751030, India

    Rahul Priyadarshi

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In this study, R.R.K. and R.P. all contributed significantly to the research efforts. R.R.K. played a key role in the execution of the experiments, while R.P. contributed to various aspects of the research process. The collaborative efforts of R.R.K. and R.P. are evident in the combined writing and development of the paper.

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Kumar, R.R., Priyadarshi, R. Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19313-6

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