Segmentation and Detection of Tumor in MRI images Using CNN and SVM Classification

Vinoth, R.; Venkatesh, C.

doi:10.1109/icedss.2018.8544306

Cited by 40 publications

(13 citation statements)

References 12 publications

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“…Kebir et al [15] proposed a supervised method for detecting the brain abnormalities from the MRI images in three steps, first step is to develop a deep learning CNN model, then a subdivision of brain MRI images is done by the k-mean algorithm followed by brain component classification as normal or abnormal classes according to the developed CNN model. Vinoth et al [16] proposed a programmed division strategy based on CNN. Here, kernels are used for classification, and SVM classification is performed with the calculated parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

Majib¹,

Rahman²,

Sazzad³

et al. 2021

IEEE Access

131

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A brain tumor is a life-threatening neurological condition caused by the unregulated development of cells inside the brain or skull. The death rate of people with this condition is steadily increasing. Early diagnosis of malignant tumors is critical for providing treatment to patients, and early discovery improves the patient's chances of survival. The patient's survival rate is usually very less if they are not adequately treated. If a brain tumor cannot be identified in an early stage, it can surely lead to death. Therefore, early diagnosis of brain tumors necessitates the use of an automated tool. The segmentation, diagnosis, and isolation of contaminated tumor areas from magnetic resonance (MR) images is a prime concern. However, it is a tedious and time-consuming process that radiologists or clinical specialists must undertake, and their performance is solely dependent on their expertise. To address these limitations, the use of computer-assisted techniques becomes critical. In this paper, different traditional and hybrid ML models were built and analyzed in detail to classify the brain tumor images without any human intervention. Along with these, 16 different transfer learning models were also analyzed to identify the best transfer learning model to classify brain tumors based on neural networks. Finally, using different state-of-the-art technologies, a stacked classifier was proposed which outperforms all the other developed models. The proposed VGG-SCNet's (VGG Stacked Classifier Network) precision, recall, and f1 scores were found to be 99.2%, 99.1%, and 99.2% respectively.

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Section: Literature Reviewmentioning

confidence: 99%

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

Majib¹,

Rahman²,

Sazzad³

et al. 2021

IEEE Access

131

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“…Vinoth et al [6] proposing the programmed division strategy based on Convolutional Neural Network (CNN). For differentiation of tumor, the kernels are used here.…”

Section: Literature Surveymentioning

confidence: 99%

An Optimal Region Growing Segmentation Algorithm with Decision Tree Tumor Classifier

Sivakumar¹,

Janakiraman²

2021

Lecture Notes in Electrical Engineering

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In the present day, to enhance the survival rate of the patients and the treatment procedure the detection of brain tumor segmentation plays an important role in the medical field. A large range of MRI images are generated at a time we cannot easily diagnose the brain tumor and it will increase the execution time and not easy to fragment in a short duration of time. Within a short time, we can easily segment and categorize the brain tumor, the automated brain tumor segmentation is used. In the proposed system, the modified region growing algorithm with a modified whale optimization algorithm used to segment the brain tumor segmentation and the MRI image will be classified by decision tree classifier technique. For preprocessing the MRI images in the brain tumor segmentation, the least mean square (LMS) technique is used. From the simulation, the proposed method gives the outcomes with a clear accuracy of 96.6%. MATLAB is used to implement this proposed strategy.

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“…To reduce overfitting they used add dropout and speed up training with the help of batch normalization technique. Moreover, to correct the bias field distortion of MRI images, they have added N4ITK method before intensity normalization to get the actual result [34]. Md.…”

Section: Literature Reviewsmentioning

confidence: 99%

Detection and 3D Visualization of Brain Tumor using Deep Learning and Polynomial Interpolation

Tuhin

Pramanick

Emon

et al. 2020

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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Among different imaging techniques MRI, MRSI and CT scans are some of the widely use techniques to visualize brain structures to point out brain anomalies especially brain tumor. Identification of brain tumor accurately in clinical practices has always been a hard decision for neurologist as multiple exceptions might present in images which may lead dubious suggestion from neurologist.In our proposed model we are aiming towards brain tumor detection and 3d visualization of tumor more accurately in efficient way. Our proposed model composed of three stages such as classification of image using CNN whether any tumor exists of not; segmentation using multi thresholding to extract the detected tumor; and 3d visualization using polynomial interpolation. the proposed model enables enhancing the accuracy of tumor detection as compare to existing models as well as segmenting and 3d visualizing the detected tumor. we get 85% accuracy on our model comparing with others which is slightly more efficient in terms of classification and detection.

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Segmentation and Detection of Tumor in MRI images Using CNN and SVM Classification

Cited by 40 publications

References 12 publications

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

An Optimal Region Growing Segmentation Algorithm with Decision Tree Tumor Classifier

Detection and 3D Visualization of Brain Tumor using Deep Learning and Polynomial Interpolation

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