Brain Tumor Classification from MRI Images and Calculation of Tumor Area

Pareek, Meenakshi; Jha, C. K.; Mukherjee, Saurabh

doi:10.1007/978-981-15-0751-9_7

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…Numerous researchers have suggested using CAD to diagnose illnesses. A neutrosophic CNN is being studied for BT detection [12][13][14]. In this hybrid approach, a CNN is utilized to extract features, whereas SVM and K-nearest neighbors (KNN) are applied for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Image enhancement technologies such as contrast augmentation and midrange stretching are used in the early phases of the process to increase the quality of MRI scans. After the tumor contour was produced using a semiautomatic method by [14], 71 features were built utilizing the intensity profile, the resulting co-occurrence matrix with updated values, and also the Gabor functions. Skull stripping is a frequent preprocessing step in traditional discriminative techniques [13,14] due to drawbacks such as parameter selection or the requirement for prior information on the images as well as lengthy computation durations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…After the tumor contour was produced using a semiautomatic method by [14], 71 features were built utilizing the intensity profile, the resulting co-occurrence matrix with updated values, and also the Gabor functions. Skull stripping is a frequent preprocessing step in traditional discriminative techniques [13,14] due to drawbacks such as parameter selection or the requirement for prior information on the images as well as lengthy computation durations. According to [16], a multigrade BT classification system was built using DL.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As the world's population ages, more and more people will encounter this disease [12,13]. The condition is extremely deadly, meaning BT diagnostic research is a particularly active area of study [14]. The use of deep learning (DL) architecture, particularly convolutional neural networks (CNN), partially tackles this issue.…”

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Segmentation Using Deep Learning on MRI Images

2023

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Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model’s output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Segmentation Using Deep Learning on MRI Images

2023

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“…Similarly, Sajja et al used an integrated architecture with fuzzy C-means clustering and a support vector machine (SVM) to produce a classification error rate of 5.2 and a 94.8 percent accuracy score [21]. The critical feature was selected using principal component analysis (PCA) and Gray-level co-occurrence matrix (GLCM) to detect the presence of brain tumours and their classification into malignant and benign categories using support vector machine (SVM) [22]. Another feature extraction method utilising CNN is to cluster the mri images using a fuzzy c-means algorithm first [27].…”

Section: Related Workmentioning

confidence: 99%

Classification of brain tumor of magnetic resonance medical images using convolutional neural network approach

Verma¹,

Arora²

2022

ijhs

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The impact tumours in the brain in medical field cannot be ignored and may lead to a short life in their highest grade. Thus, conduction of proper diagnosis that too in its early stage to improve the quality of life of patients is a necessity. Normally, several image processing techniques including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) is a type of imaging that uses a (MRI) are be ingutilized to localize and calculate the size and tumor in a brain. But has limited performance for accurate quantitative measurements that too in a small number of sample images. In this project, a simple yet robust classification using Convolutional Neural Networks (CNN) for for brain cancer is proposed. The investigational outcomes with low complication is anticipated and potentially competes the relevant state of arts methods.

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Automatic Detection of Tumor Cell in Brain MRI Using Rough-Fuzzy Feature Selection with Support Vector Machine and Morphological Operation

Halder¹,

Sarkar

2021

Intelligent Systems Reference Library

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Brain Tumor Classification from MRI Images and Calculation of Tumor Area

Cited by 14 publications

References 9 publications

Brain Tumor Segmentation Using Deep Learning on MRI Images

Brain Tumor Segmentation Using Deep Learning on MRI Images

Classification of brain tumor of magnetic resonance medical images using convolutional neural network approach

Automatic Detection of Tumor Cell in Brain MRI Using Rough-Fuzzy Feature Selection with Support Vector Machine and Morphological Operation

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