Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow

Malathi, M.; Sinthia, P.

doi:10.31557/apjcp.2019.20.7.2095

Cited by 60 publications

(34 citation statements)

References 10 publications

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“…The reported work depicts the image processing technique and the simple, intelligent system like the hill-climbing algorithm. Malathi et al [ 107 ] presented the CNN method for the segmentation of brain tumors and achieved high prediction accurateness [ 132 ], compared three segmentation algorithms and proposed a Random Forest (RF) classifier, and convolution neural network. RF and CNN yielded an average Dice’s coefficient (DC) of 0.862 and 0.876, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Kumar

Gupta

Singla³

et al. 2021

Arch Computat Methods Eng

126

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Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

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Section: Related Workmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Kumar

Gupta

Singla³

et al. 2021

Arch Computat Methods Eng

126

View full text Add to dashboard Cite

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“…In this study, a neural network is trained local and global features for segmentation and time cost is also improved with CNN parameters like max pool, max out and learning parameters. Method is evaluated with 75 percent of Dice Index [28]. Moreover in another study With a hybrid of FCM and feature, better segmentation is attained where jaccard index is 23 and dice is 34 [29].…”

Section: Iirelated Workmentioning

confidence: 99%

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

et al. 2021

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Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element.. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

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“…Other works used convolutional neural networks (CNNs) to detect brain tumors [31], [32]. With CNNs of three and four layers, the system would suffer for being time expensive and high complexity.…”

Section: Related Workmentioning

confidence: 99%

“…With CNNs of three and four layers, the system would suffer for being time expensive and high complexity. The work presented in [31] has relied on big data methods but without giving explicit description on the data used nor providing any details on the utilization of the veracity, velocity or other known characteristics of big data and how they overcame these problems.…”

Section: Related Workmentioning

confidence: 99%

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Abbas

Mahmoud

2021

IJECE

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Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.

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Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow

Cited by 60 publications

References 10 publications

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

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