A systematic review on a comparative study of AI techniques for the classification of brain tumour

Laiba Junaid; Javeria Noor; Aqsa Bibi; Jawwad Sami Ur Rahman; Faraz Akram; Shahid Hussain Khan; Sathish Kumar Selvaperumal

doi:10.47264/idea.ajset/3.1.8

Authors

Laiba Junaid Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan. https://orcid.org/0009-0000-5303-7964
Javeria Noor Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan. https://orcid.org/0009-0008-5058-0281
Aqsa Bibi Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan. https://orcid.org/0009-0004-0918-873X
Jawwad Sami Ur Rahman Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan. https://orcid.org/0000-0002-1945-3339
Faraz Akram Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan. https://orcid.org/0000-0003-1483-1801
Shahid Hussain Khan Department of Biomedical Engineering, NED University of Engineering and Technology, Karachi, Pakistan. https://orcid.org/0009-0001-7220-3830
Sathish Kumar Selvaperumal College of Engineering, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia. https://orcid.org/0000-0002-3848-9441

DOI:

https://doi.org/10.47264/idea.ajset/3.1.8

Keywords:

Magnetic resonance imaging, Artificial neural network, Softmax regression, Support vector machine, Segmentation, Genetic Algorithm, Convolutional neural network

Abstract

This paper explores advances in Machine Learning (MI) techniques for detecting brain tumours using Magnetic Resonance Imaging (MRI) images. Previous research used a variety of algorithms, although there are disparities in terms of efficacy, reliability, computational complexity, and execution time. An early and exact diagnosis of brain tumours is required to improve treatment success and patient survival. This study thoroughly examines existing MI and newly invented deep learning algorithms, emphasising their performance in detecting, segmenting, and classifying tumour images. Furthermore, it examines the most recent advances in cancer grade classification and segmentation, emphasising the use of MI, digital image processing, and medical experience to increase diagnosis accuracy. Notable models, such as deep neural networks and Artificial Neural Networks (ANNs), have achieved accuracy rates of 98%, with some models nearing 99%. This study seeks to give a clear roadmap for future research in the rapidly growing field of brain tumour diagnostics, emphasizing the importance of imaging technologies such as MRI and sophisticated computational tools in early identification and treatment planning. The evaluation intends to provide a guide for further investigations in the developing field of brain tumour diagnostics.

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