Journal ArticleDOI
Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images
TLDR
A combination of Artificial Neural Network and Fuzzy K-means algorithm has been presented to segment the tumor locale and the overall accuracy has been improved by 8% when compared with K-Nearest Neighbor methodology.Abstract:
The primary objective of this paper is to develop a methodology for brain tumor segmentation. Nowadays, brain tumor recognition and fragmentation is one among the pivotal procedure in surgical and medication planning arrangements. It is difficult to segment the tumor area from MRI images due to inaccessibility of edge and appropriately visible boundaries. In this paper, a combination of Artificial Neural Network and Fuzzy K-means algorithm has been presented to segment the tumor locale. It contains four phases, (1) Noise evacuation (2) Attribute extraction and selection (3) Classification and (4) Segmentation. Initially, the procured image is denoised utilizing wiener filter, and then the significant GLCM attributes are extricated from the images. Then Deep Learning based classification has been performed to classify the abnormal images from the normal images. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 94%, sensitivity of 98% specificity of 99%, Jaccard index of 96%. The overall accuracy of this proposed technique has been improved by 8% when compared with K-Nearest Neighbor methodology.read more
Citations
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Journal ArticleDOI
A Hybrid Deep Learning-Based Approach for Brain Tumor Classification
Asaf Raza,Huma Ayub,J. Khan,Ijaz Ahmad,Ahmed S. Salama,Yousef Ibrahim Daradkeh,Danish Javeed,Ateeq Ur Rehman,Habib Hamam +8 more
TL;DR: The proposed hybrid deep learning model called DeepTumorNet for three types of brain tumors—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture showed its superiority over the existing models for BT classification from the MRI images.
Journal ArticleDOI
A Survey of Brain Tumor Segmentation and Classification Algorithms.
Erena Siyoum Biratu,Friedhelm Schwenker,Yehualashet Megersa Ayano,Taye Girma Debelee,Taye Girma Debelee +4 more
TL;DR: A comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning, can be found in this paper.
Journal ArticleDOI
A hybrid deep CNN-Cov-19-Res-Net Transfer learning architype for an enhanced Brain tumor Detection and Classification scheme in medical image processing
TL;DR: Hyb-DCNN-ResNet 152 TL weight parameters are tuned using Covid-19 optimization algorithm (CoV-19 OA). The simulation process is executed in the MATLAB platform as mentioned in this paper .
Journal ArticleDOI
Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey
Noureen Naz Talpur,Said Jadid Abdulkadir,Hitham Alhussian,Mohd Hilmi Hasan,NorShakirah Bt A Aziz,Alwi M. Bamhdi +5 more
TL;DR: In this paper , a systematic review of deep neuro-fuzzy systems (DNFS) studies is performed to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope.
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