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- W3170804644 abstract "MRI based lesion detection is an essential step for computer aided diagnosis to detect brainlesion. There are several types of tissues, skull portion in brain MRI images, which lead to falsedetection of brain lesion. To get appropriate lesion detection segmentation architecture based onConvolution Neural Network (CNN) is proposed in the study. Here, 2450 3D RGB images are takenfrom The Cancer Genome Atlas (TCGA), where 2400 images are used from 104 patients for trainingpurpose and 50 images are taken from six patients for testing purpose. From the test images thearchitecture achieved accuracy (99.1%), dice similarity (85.4%), jaccard index (74.8%), Mathewscorrelation coefficient (82.2%), sensitivity (89.7%), specificity (99.3%), and precision (82.3%) with95% confidence interval. Besides, the receiver operating characteristic (ROC) curve also plotted withhighest 97.22% area, which proves the constancy and reliability of the architecture. The proposed CNNbased architecture detects the accurate lesion from brain MRI 3D RGB images." @default.
- W3170804644 created "2021-06-22" @default.
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- W3170804644 date "2021-01-01" @default.
- W3170804644 modified "2023-09-24" @default.
- W3170804644 title "Brain Lesion Detection: An Architecture Based On Convolution Neural Network" @default.
- W3170804644 hasPublicationYear "2021" @default.
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