An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks

Thanh, Hán Trọng; Van, Hinh Nguyen; Thanh, Huong Nguyen Thi; Vu, Tran Anh; Tuan, Dung Nguyen; Vu, Dang Luu

doi:10.1155/2022/2092985

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Dataset: Public Domain CCO licensed by Kaggle. [95] Presents a system that uses ADAS optimization and artificial intelligence algorithms to detect brain cancers on MRIs accurately. One of the most hazardous diseases for humans is one that this technology is designed to help doctors diagnose.…”

Section: Studies Published In 2021mentioning

confidence: 99%

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Neamah,

Mohamed,

Adnan

et al. 2024

IEEE Access

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In recent years, the realms of computer vision and deep learning have ushered in transformative changes across various domains. Among these, deep learning stands out for its remarkable capacity to handle vast datasets, revolutionizing numerous fields, including the biomedical sector. In particular, its prowess has been harnessed in the realm of brain tumor identification through MRI scans, yielding impressive results. This research project is dedicated to conducting an exhaustive exploration of existing endeavors in the domain of brain tumor identification and classification via MRI scans. This endeavor is poised to be of profound value to researchers looking to leverage their deep learning expertise in the realm of brain tumor detection and categorization. The initial phase involves an overview of prior studies that have employed deep learning for categorising and detecting brain tumors. Subsequently, a meticulous analysis of deep learning studies proposed in research publications spanning (2019 to 2022) is presented in tabular form. The conclusion section comprehensively assesses the merits and demerits inherent in deep neural networks. The insights gleaned from this study promise to equip future researchers with a holistic perspective on current research trends and a nuanced understanding of the effectiveness of diverse deep learning methodologies. It is our fervent belief that this research will significantly advance the understanding of brain tumors and their detection methodologies.

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Section: Studies Published In 2021mentioning

confidence: 99%

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Neamah,

Mohamed,

Adnan

et al. 2024

IEEE Access

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“…Sharif et al's recent work [20] was carried out using many procedures for classifying brain tumors using different computer-aided methods. However, the low accuracy produced by these applied methods was a significant concern.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach

et al. 2023

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Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.

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“…In [14], an artificial intelligence-based tumor classification approach is discussed, which normalizes the brain image and applies an (Advanced Driver Assistance Systems) ADAS optimization function with Deep CNN towards classification. In [15], a multi-source correlation network is presented to learn the multi-source correlation towards handling missing data in brain image classification.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

Panyala¹,

Baskar²

2023

Computer Systems Science and Engineering

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The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are extracted from the images. Extracted features are trained with deep learning Convolution Neural Network (CNN). The network has been designed with a single convolution layer towards dimensionality reduction. The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix, which has been transformed into a single-dimensional feature vector at the convolution layer. The neurons of the intermediate layer are designed to measure White Mass Texture Support (WMTS), Gray Mass Texture Support (GMTS), White Mass Covariance Support (WMCS), Gray Mass Covariance Support (GMCS), and Class Texture Adhesive Support (CTAS). In the test phase, the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images. Based on that, the method adds a Multi-Variate Feature Similarity Measure (MVFSM). Based on the importance of MVFSM, the process finds the class of brain image given and produces an efficient result.

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An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks

Cited by 8 publications

References 31 publications

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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