Journal ArticleDOI
K -Means clustering and neural network for object detecting and identifying abnormality of brain tumor
N. Arunkumar,Mazin Abed Mohammed,Mazin Abed Mohammed,Mohd Khanapi Abd Ghani,Dheyaa Ahmed Ibrahim,Enas Abdulhay,Gustavo Ramirez-Gonzalez,Victor Hugo C. de Albuquerque +7 more
- Vol. 23, Iss: 19, pp 9083-9096
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TLDR
An improved automated brain tumor segmentation and identification approach using ANN from MR images without human mediation is shown by applying the best attributes toward preparatory brain tumor case revelation.Abstract:
Brain tumor diagnosis is a challenging and difficult process in view of the assortment of conceivable shapes, regions, and image intensities. The pathological detection and identification of brain tumor and comparison among normal and abnormal tissues need grouped scientific techniques for features extraction, displaying, and measurement of the disease images. Our study shows an improved automated brain tumor segmentation and identification approach using ANN from MR images without human mediation by applying the best attributes toward preparatory brain tumor case revelation. To obtain the exact district region of brain tumor from MR images, we propose a brain tumor segmentation technique that has three noteworthy improvement focuses. To begin with, K-means clustering will be utilized as a part of the principal organization in the process of improving the MR image to be marked in the districts regions in light of their gray scale. Second, ANN is utilized to choose the correct object in view of training phase. Third, texture feature of brain tumor area will be extracted to the division stage. With respect to the brain tumor identification, the grayscale features are utilized to analyze and diagnose the brain tumor to differentiate the benign and malignant cases. According to the study results demonstrated that: (1) enhancement adaptive strategy was utilized as post-processing in brain tumor identification; (2) identify and build an assessment foundation of automated segmentation and identification for brain tumor cases; (3) highlight the methods based on region growing method and K-means clustering technique to select the best region; and (4) evaluate the proficiency of the foreseen outcomes by comparing ANN and SVM segmentation outcomes, and brain tumor cases classification. The ANN approach classifier recorded accuracy of 94.07% with line assumption (brain tumor cases classification) and sensitivity of 90.09% and specificity of 96.78%.read more
Citations
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Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images
TL;DR: A mathematical model P F S E C TL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task.
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Deep learning based enhanced tumor segmentation approach for MR brain images
TL;DR: Deep learning-based approach is proposed for brain tumor image segmentation and proves that the proposed technique has outperformed SVM and CNN in terms of accuracy, PSNR, MSE and other performance parameters.
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Voice Pathology Detection and Classification Using Convolutional Neural Network Model
Mazin Abed Mohammed,Karrar Hameed Abdulkareem,Salama A. Mostafa,Mohd Khanapi Abd Ghani,Mashael S. Maashi,Begonya Garcia-Zapirain,Ibon Ruiz Oleagordia,Hosam Alhakami,Fahad Taha AL-Dhief +8 more
TL;DR: A pre-trained Convolutional Neural Network was applied to a dataset of voice pathology to maximize the classification accuracy and a distinguished training method combined with various training strategies is proposed in order to generalize the application of the proposed system on a wide range of problems related to voice disorders.
Proceedings ArticleDOI
Machine learning and Region Growing for Breast Cancer Segmentation
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Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning
Johnathon M. Shook,Tryambak Gangopadhyay,Linjiang Wu,Baskar Ganapathysubramanian,Soumik Sarkar,Asheesh K. Singh +5 more
TL;DR: A Long Short Term Memory—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments and developed a temporal attention mechanism for LSTM models.
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