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Application of Machine Learning Techniques for Detection and Segmentation of Brain Tumors

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Abstract

A brain tumor develops when cells multiply rapidly out of control. There is a risk of death if it is not treated in the early stages. Accurate segmentation and classification are still difficult, despite many significant efforts and promising outcomes. The wide range of tumor locations, shapes and sizes causes a significant obstacle in the field of brain tumor diagnosis. The goal of this study is to provide a comprehensive analysis of brain tumor detection malignant or benign using different features of the dataset. Our proposed model focused on the application of Machine Learning Techniques using an ensemble method to develop and classify them into malignant or benign brain tumors. The overall analysis is divided into two parts: first, we extract 30 attributes related to brain tumors from MR images, where datasets are publicly available. After that, we used the ensemble method to detect the tumors from said attributes and segment them into two categories malignant or benign tumors. The outputs of our model give robustness and cross-validation revealing to the accuracy, precision, recall, and AUC as 95.26%, 95.55%, 97.21%, and 96%, respectively. This study proposed a method of dividing the brain tumor with minimal human intervention. The goal of the proposed model is to reduce identification time so that neurosurgeons can get back to saving lives. The experimental results suggest that the method is nearly as accurate as the best existing methods.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request (https://figshare.com/articles/dataset/brain_tumor_dataset/1512427).

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Authors and Affiliations

  1. Sri Sri University, Cuttack, Odisha, India

    Bhagirathi Nayak & Guru Prasad Dash

  2. VNR VJIET, Hyderabad, Telangana, India

    Rajesh Kumar Ojha

  3. Saraswati Degree Science College, Cuttack, Odisha, India

    Sunil Kumar Mishra

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  1. Bhagirathi Nayak
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  2. Guru Prasad Dash
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  3. Rajesh Kumar Ojha
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  4. Sunil Kumar Mishra
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Correspondence to Bhagirathi Nayak.

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This article is part of the topical collection “Machine Learning Modeling Techniques and Applications” guest edited by Lazaros Iliadis, Elias Pimenidis and Chrisina Jayne.

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Nayak, B., Dash, G.P., Ojha, R.K. et al. Application of Machine Learning Techniques for Detection and Segmentation of Brain Tumors. SN COMPUT. SCI. 4, 520 (2023). https://doi.org/10.1007/s42979-023-01918-7

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