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Machine Learning Based Intelligent Diagnosis of Brain Tumor: Advances and Challenges

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Machine Learning for Cyber Physical System: Advances and Challenges

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 60))

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Abstract

One of the fatal diseases that kills a large number of humanity across the globe is brain tumor. If the brain tumor detection is delayed, then the patient has to spend a large amount of money as well as to face severe suffering. Therefore, there is an essential need to detect the brain tumor so that money and life can be saved. The conventional examination of brain images by doctors does not reveal the presence of a tumor in a reliable and accurate manner. To overcome these issues, early and accurate brain tumor identification is of prime importance. A short while ago, methods employing machine learning (ML) and artificial intelligence (AI) were utilized to properly diagnose other diseases using test attributes, electrocardiogram (ECG), electromyography (EMG), Heart Sounds, and other types of signals obtained from the human body. This chapter presents a complete overview of the detection of patient-provided brain MR pictures and classifying patients’ brain tumor using AI and ML approaches. For this pose, brain images obtained from kaggle.com website have been employed for develo** various AI and ML classifiers. Through simulation-based experiments conducted on the AI and ML classifiers, performance matrices have been obtained and compared. From the analysis of results reported in the different articles, it is observed that Random Forest exhibit superior detection of brain tumor. There is still further scope for improving the performances as well as develo** affordable, reliable, and robust AI-based brain tumor classifiers.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, C. V. Raman Global University, Odisha, India

    Surendra Kumar Panda & Ram Chandra Barik

  2. Faculty of Communication Sciences, Teramo, Italy

    Danilo Pelusi

  3. Department of Electronics and Communication Engineering, C. V. Raman Global University, Odisha, India

    Ganapati Panda

Authors
  1. Surendra Kumar Panda
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  2. Ram Chandra Barik
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  3. Danilo Pelusi
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  4. Ganapati Panda
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Corresponding author

Correspondence to Surendra Kumar Panda .

Editor information

Editors and Affiliations

  1. Department of Computer Science, MSCB University, Baripada, India

    Janmenjoy Nayak

  2. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, India

    Bighnaraj Naik

  3. Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Vimal S

  4. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita Favorskaya

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Panda, S.K., Chandra Barik, R., Pelusi, D., Panda, G. (2024). Machine Learning Based Intelligent Diagnosis of Brain Tumor: Advances and Challenges. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_11

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