Brain Tumor Classification from MRI Images Using Convolutional Neural Network

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

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Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches

Ashimgaliyev,

Matkarimov,

Barlybayev

et al. 2024

Applied Sciences

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Magnetic Resonance Imaging (MRI) is vital in diagnosing brain tumours, offering crucial insights into tumour morphology and precise localisation. Despite its pivotal role, accurately classifying brain tumours from MRI scans is inherently complex due to their heterogeneous characteristics. This study presents a novel integration of advanced segmentation methods with deep learning ensemble algorithms to enhance the classification accuracy of MRI-based brain tumour diagnosis. We conduct a thorough review of both traditional segmentation approaches and contemporary advancements in region-based and machine learning-driven segmentation techniques. This paper explores the utility of deep learning ensemble algorithms, capitalising on the diversity of model architectures to augment tumour classification accuracy and robustness. Through the synergistic amalgamation of sophisticated segmentation techniques and ensemble learning strategies, this research addresses the shortcomings of traditional methodologies, thereby facilitating more precise and efficient brain tumour classification.

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Brain Tumor Classification from MRI Images Using Convolutional Neural Network

Cited by 3 publications

References 16 publications

Review, Limitations, and future prospects of neural network approaches for brain tumor classification

Review, Limitations, and future prospects of neural network approaches for brain tumor classification

A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches

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