Graph Theory Based Segmentation of Magnetic Resonance Images for Brain Tumor Detection

Mamatha, S. K.; Krishnappa, H K; Shalini, N.

doi:10.1134/s1054661821040167

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…Shree and Kumar [11], utilized MR data extracted features using a grey-level co-occurrence matrix (GLCM) and applied discrete wavelet transform (DWT) with a region-growing segmentation method, achieving an accuracy of 98.02%. Mamatha et al [12], introduced a graph theory based segmentation method in which a weighted directed graph is constructed. Each pixel in the image is represented as a nodes, and paths are obtained for the detection of MR brain tumors before the segmentation process.…”

Section: Related Workmentioning

confidence: 99%

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Singh,

Saxena

2024

Int. j. electr. comput. eng. syst. (Online)

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Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists' expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth.

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Section: Related Workmentioning

confidence: 99%

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Singh,

Saxena

2024

Int. j. electr. comput. eng. syst. (Online)

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“…In the field of medical science, a number of applications that use various machine learning techniques are presently being used for data analysis and innovation. Machine learning techniques have been used in a number of recent healthcare research studies, including the diagnosis of COVID-19 using X-rays [ 12 , 13 ], the identification of tumors using MRIs [ 14 , 15 ], the prediction of cardiovascular diseases [ 16 , 17 ], dengue [ 18 , 19 ], stroke [ 20 ], and cancer [ 21 , 22 ]. Kader et al [ 23 ] created a model that employed feature selection and extraction strategy to predict Alzheimer's disease using machine learning techniques.…”

Section: Review Of Machine Learning Usage In Medical Clinical Diagnos...mentioning

confidence: 99%

A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease

Uddin

Alam

Jannat-E-Anawar

et al. 2023

Biomedical Materials & Devices

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Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection.

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“…The main idea of their approach is to use a biologically inspired orthogonal wavelet transform and deep learning techniques. Techniques of graph theory were used [31] to detect abnormalities in brains. A VGG16 architecture was the main model to classify brain images in [32].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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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Graph Theory Based Segmentation of Magnetic Resonance Images for Brain Tumor Detection

Cited by 15 publications

References 21 publications

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach

A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease

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

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