Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function

Huang, Zhiguan; Du, Xiaohao; Chen, Liangming; Li, Yuhe; Liu, Mei; Chou, Yao; Jin, Long

doi:10.1109/access.2020.2993618

Cited by 101 publications

(30 citation statements)

References 48 publications

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“…They utilized 220 brain MR images and attained classification accuracy of 94.5%. The authors of [ 43 ] devised a CNN network model for utilizing MRI data to classify brain tumors into distinct groups. The proposed model emphasized on complex patterns with modified activation functions.…”

Section: Comparison Of the Proposed Methods With The State-of-the-art...mentioning

confidence: 99%

A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI

Haq

Huang

et al. 2022

Computational and Mathematical Methods in Medicine

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Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model’s performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.

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Section: Comparison Of the Proposed Methods With The State-of-the-art...mentioning

confidence: 99%

A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI

Haq

Huang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The results showed VGG19 achieved better performance, with an average accuracy of 94.82 for brain tumor classification. Huang et al [ 68 ] proposed a deep CNN model with a modified activation function for brain tumor classification. The CNN model is constructed automatically by a network generator based on three different graph generation algorithms.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…From Table 4 , it can be seen that in comparison to existing techniques, the proposed approach shows an improved overall accuracy of 98.34% for brain tumor type classification. The above-mentioned approaches [ 66 , 67 , 68 ] extract features from the whole image which may result in the misclassification of tumor type due to the complex nature of the tumor, i.e., overlapping boundaries and MRI artifacts. In [ 69 ], hand-crafted features are employed that are less discriminative and robust.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

et al. 2021

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A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

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“…Their proposed CNNBCN system reached 94.53% accuracy. Although CNN models achieved better results than their CNNBCN method in diagnosing brain tumours, their method enriches CNN design [ 21 ]. Kaur et al implemented several pretrained deep convolutional neural networks (DCNNs), namely, AlexNet, GoogLeNet, ResNet101, ResNet50, VGG16, InceptionV3, and InceptionResNetV2, where they replaced the last layers of these models to suit the new classes of images.…”

Section: Related Workmentioning

confidence: 99%

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Senan

Jadhav

Rassem

et al. 2022

Computational and Mathematical Methods in Medicine

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Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients’ chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.

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Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function

Cited by 101 publications

References 48 publications

A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI

A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

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