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Enhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 825))

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Abstract

Medical imaging analysis plays a critical role in the medical field, transforming how diseases are found, diagnosed, and treated. The integration of machine learning and deep learning has dramatically advanced the field of medical image analysis, leading to the creation of more advanced algorithms for improved diagnosis and disease detection. This study examines the impact of these cutting-edge technologies on the accuracy of medical imaging analysis. It investigates the most effective algorithms and techniques currently used, as well as how different types of medical images impact the accuracy and efficiency of these algorithms. The limitations and challenges faced during implementation and their effect on healthcare professionals’ decision-making are also explored. This research provides a comprehensive understanding of the state of the art in medical image analysis through machine learning and deep learning, highlighting recent developments and their practical applications.

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Authors and Affiliations

  1. Universiti Teknologi Petronas, 79 LakeVille Seri Iskandar 32610, Perak, Malaysia

    Usman Ahmad Usmani

  2. LUT University, Yliopistonkatu 34, 53850, Lappeenranta, Finland

    Ari Happonen

  3. 1 Chome-104 Totsukamachi, Shinjuku, Tokyo, 169-8050, Japan

    Junzo Watada

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  1. Usman Ahmad Usmani
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  2. Ari Happonen
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  3. Junzo Watada
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Correspondence to Ari Happonen .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Usmani, U.A., Happonen, A., Watada, J. (2024). Enhancing Medical Diagnosis Through Deep Learning and Machine Learning Approaches in Image Analysis. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 825. Springer, Cham. https://doi.org/10.1007/978-3-031-47718-8_30

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