Brain Tumor Detection and Segmentation by Intensity Adjustment

Rajan, P.K.; Sundar, C.

doi:10.1007/s10916-019-1368-4

Cited by 72 publications

(23 citation statements)

References 19 publications

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“…Several efforts have been made to develop a highly accurate and robust solution for MRI-based brain tumor classification using various ML classifiers: neural network classifier [ 8 , 21 , 64 ], Naïve Bayes classifier [ 65 ], AdaBoost classifier [ 66 ], k-NN classifier [ 64 ], RF classifier [ 64 , 67 ], SVM classifier [ 18 , 22 ], and ELM classifier [ 68 ]. However, there have been no studies done on evaluating the effectiveness of ML classifiers for the MRI-based brain tumor classification task.…”

Section: Proposed Methodsmentioning

confidence: 99%

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MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

271

140

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Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

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Section: Proposed Methodsmentioning

confidence: 99%

“…Finally, these extracted features were then fed into feed-forward backpropagation neural network and obtained a high accuracy rate. Rajan and Sundar [ 22 ] proposed a hybrid energy-efficient method for automatic tumor detection and segmentation. Their proposed method is comprised of seven long phases and reported 98% accuracy.…”

Section: Related Workmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

271

140

View full text Add to dashboard Cite

show abstract

“…The approach in [41] exhibits better tumor segmentation results; however, it is unable to identify a tumor of small size. Rajan et al [43] presented a method for segmenting tumorous tissues from the human brain. After preprocessing, K-Mean clustering along with Fuzzy C-Means was applied over the input sample to obtain the image clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the SVM classifier was trained to detect the tumor region. The approach in [43] improves the computational complexity of the segmentation process; however, for large intensity variation in the input sample, it may not perform well. Sharif et al [44] proposed a framework to detect and segment the brain tumor.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Brain MRI Images Using Improved CornerNet Approach

et al. 2021

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The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

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“…The authors in [28] combined k-means clustering, fuzzy c-means, and active contour by level set in a unified framework for brain tumor segmentation. Herein, employing the intensity adjustment process improved the segmentation accuracy.…”

Section: Related Workmentioning

confidence: 99%

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

et al. 2020

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Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

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Brain Tumor Detection and Segmentation by Intensity Adjustment

Cited by 72 publications

References 19 publications

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Analysis of Brain MRI Images Using Improved CornerNet Approach

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

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