An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

Wu, Wentao; Li, Daning; Du, Jinpeng; Gao, Xiangyu; Gu, Wen; Zhao, Fanfan; Feng, Xinbin; Yan, Hong

doi:10.1155/2020/6789306

Cited by 91 publications

(42 citation statements)

References 33 publications

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“…Wu et al (2020) proposed an intelligent diagnosis method of Brain MRI Tumor segmentation. This work was performed by deep convolutional neural network and SVM algorithm which consists of three stages.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan¹,

Ramasamy²

2021

Concurrent Engineering

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Nowadays, the most demanding and time consuming task in medical image processing is Brain tumor segmentation and detection. Magnetic Resonance Imaging (MRI) is employed for creating a picture of any part in a body. MRI provides a competent quick manner for analyzing tumor in the brain. This proposed framework contains different stages for classifying tumor like Preprocessing, Feature extraction, Classification, and Segmentation. Initially, T1-weighted magnetic resonance brain images are considered as an input for computational purpose. Median filter is proposed to optimize the skull stripping in MRI images. Abnormal brain tissues are extracted in low contrast, in addition to meticulous location of edges of affected tissue can be detected. Then, Discrete Wavelet Transform (DWT) and Histogram of Oriented Gradients (HOG) are performing feature extraction process. HOG is used for extracting the features like texture and shape. Then, Classification is performed through Machine learning categorization techniques via Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). These classifiers classify the brain image as either normal or abnormal and the performance is analyzed by various parameters such as sensitivity, specificity and accuracy.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan¹,

Ramasamy²

2021

Concurrent Engineering

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“…To classify a set of images, a labelled dataset is required which includes a set of images with labels; these labels might be Yes/No or types of tumour. This dataset is used to train Convolutional Neural Network, Support Vector Machine [18], or Deep Learning algorithms [30], [12]. The algorithm is then used to predict the MR images status.…”

Section: Ai-based Approachesmentioning

confidence: 99%

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Al-Azawi¹

2021

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Due to the great development in the field of digital image processing, it has become an integral and important part of computer-aided diagnosis systems. Magnetic resonance image (MRI) processing is one of the hot fields that attracted researchers due to its role in improving and speeding up diagnosis. This work presents a new method for identifying abnormal regions in brain MR images using saliency recognition techniques. This is because tumour regions or abnormal regions share with saliency the fact that these regions differ from the rest of the image in terms of size, luminance, and texture. In this paper, we use a saliency extraction method that uses the irregularity of the regions in its definition, as this method depends on the basis that the salient areas in the image are rare and different from the rest of the image, which is what we need to determine the tumour area. The proposed algorithm is applied to a standard dataset and the obtained result showed a high level of accuracy, where the precision, recall, F-measure, and accuracy averages reached 91.8%, 96.2%, 84.5%, and 96% respectively. The results were discussed thoroughly, and the limitations were identified and discussed as well.

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“…Wu et al [14] developed a DCNN fused with SVM to segment brain cancer images with the aid of three different processes. First, a DCNN was trained to learn the mapping from image space to cancer sign space.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Nagarajan¹,

Tajunisha²

2021

RIA

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The classification of ovarian cancer types is a very challenging process for physicians' eyes. To solve this problem, this article proposes a new deep learner, which classifies ovarian cancer types from Computerized Tomography (CT) images. Firstly, a Deep Convolutional Neural Network (DCNN) model depending on AlexNet is proposed to categorize ovarian cancer from CT images. But its efficiency is not satisfactorily high. So, DCNN is built based on the fusion of AlexNet, VGG, and GoogLeNet. The fusion is carried out at the SoftMax layer by fusing the SoftMax values of each network structure using a weighted sum to obtain the overall classification outcome. But overfitting problems can occur due to an inadequate number of training images. Thus, a Deep Semi-Supervised Generative Learning with DCNN model (DSSGL-DCNN) is proposed by using a Generative Adversarial Network (GAN) which augments the training samples to solve the overfitting problem. Once the augmented dataset is obtained, the fused DCNN model is learned to classify ovarian cancer types. Further, the classified outcomes can be used as a useful guideline for physicians in medical diagnosis. Finally, the experimental results show that the DSSGL-DCNN achieves higher efficiency compared to the other DCNN architectures.

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An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

Cited by 91 publications

References 33 publications

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

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