An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification

Maqsood, Sarmad; Damaševičius, Robertas; Shah, Faisal Mehmood

doi:10.1007/978-3-030-86976-2_8

Cited by 62 publications

(39 citation statements)

References 33 publications

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“…In the latest studies, pre-trained CNNs have proved to be excellent in the automatic diagnosis of cognitive disease from brain MR images. AlexNet [14,15], VGG16 [16,17], VGG11 [18], ResNet-34 [19], ResNet-50 [20], U-Net [21,22], SqueezeNet and InceptionV3 [23], and DenseNet201 are examples of pre-trained deep neural networks that have been effectively used in MRI analysis. Compared to a model that comprises a single network pre-trained on MRI slices, multiple pre-trained networks on a large scale with MRI may gather potentially useful functional and structural information for discriminating the AD stages.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.

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Section: Introductionmentioning

confidence: 99%

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

Self Cite

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“…The above measures help to get the related values, such as the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR) as discussed in [ 26 – 30 ]. Along with these measures, the other vital metrics, such as Jaccard, Dice, accuracy, precision, sensitivity, specificity, and negative predictive values (NPV), are also computed, and based on these values, the merit of the CNN is confirmed.…”

Section: Methodsmentioning

confidence: 99%

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

Krishnamoorthy

Zhang

Kadry³

et al. 2022

Computational Intelligence and Neuroscience

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Multiple sclerosis (MS) is an autoimmune disease that causes mild to severe issues in the central nervous system (CNS). Early detection and treatment are necessary to reduce the harshness of the disease in individuals. The proposed work aims to implement a convolutional neural network (CNN) segmentation scheme to extract the MS lesion in a 2D brain MRI slice. To achieve a better MS detection, this work implemented the VGG-UNet scheme in which the pretrained VGG19 is considered as the encoder section. This scheme is tested on 30 patient images (600 images with dimension 512 × 512 × 3 pixels), and the experimental outcome confirms that this scheme provides a better result compared to traditional UNet, SegNet, VGG-UNet, and VGG-SegNet. The experimental investigation implemented on axial, coronal and sagittal plane 2D slices of Flair modality confirms that this work provides a better value of Jaccard (>85%), Dice (>92%), and accuracy (>98%).

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“…The system achieved 96.49% accuracy. Maqsood et al [ 19 ] suggested a brain tumor detection method employing edge detection and U-NET model. The tumor segmentation framework enhances image contrast and performs edge detection fuzzy logic.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers

Kibriya

Amin

Alshehri

et al. 2022

Computational Intelligence and Neuroscience

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Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method’s classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.

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An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification

Cited by 62 publications

References 33 publications

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers

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