Early identification and diagnosis of brain tumor using a supervised approach plays an essential role in the field of medicine. In this paper, an automated computer-aided method using deep learning architecture named CNN Deep net is proposed for the detection, classification and diagnosis of meningioma brain tumor. This proposed method includes preprocessing, classification, and segmentation of the primary occurring brain tumor in adults. The proposed CNN Deep Net architecture extracts the features internally from enhanced image and classifies them into normal and abnormal tumor images. The segmentation of tumor region is performed by the global thresholding with area morphological function method. This proposed method of fully automated classification and segmentation of brain tumor preserves the spatial invariance and inheritance. Furthermore, based on its feature attributes the proposed CNN Deep net classifier, classifies the detected tumor image belongs either to (low grade)benign or (high grade)malignant. This proposed CNN Deep net classification methodology approach with grading system is evaluated both quantitatively and qualitatively. The quantitative measures such as sensitivity, specificity, accuracy, Dice similarity coefficient, precision, F-score of the proposed classification and segmentation methodology states a better segmentation accuracy and classification rate of 99.4% and 99.5% with respect to ground truth images.
The detection of brain tumors in brain magnetic resonance imaging (MRI) image is an important process for preventing earlier death. This article proposes an automated computer aided method for detecting and locating the brain tumors in brain MRI images using deep learning algorithms. The proposed method has three sub modules as preprocessing, classifications and segmentation. In this article, data augmentation is used as preprocessing method. The preprocessed brain MRI images are classified into either tumor case or nontumor case using classification approach. In this brain tumor detection and segmentation process, convolutional neural networks (CNNs) classification architecture is used for classifying the brain images. The morphological based segmentation methodology is used in this article for segmenting the tumor regions in classified brain images. Further, the segmented tumor regions are diagnosed into "Mild" and "Severe" case using CNN architecture. The proposed methodology is applied on the brain images from open access dataset. The performance of the proposed system is analyzed in terms of sensitivity, specificity, and precision, F-score, Disc similarity index and tumor region segmentation accuracy on set of brain images. The simulation results of this proposed framework are verified by expert radiologist.
Early identification and diagnosis of brain tumor using a supervised approach plays an essential role in the field of medicine. In this paper, an automated computer-aided method using deep learning architecture named CNN Deep net is proposed for the detection, classification and diagnosis of meningioma brain tumor. This proposed method includes preprocessing, classification, and segmentation of the primary occurring brain tumor in adults. The proposed CNN Deep Net architecture extracts the features internally from enhanced image and classifies them into normal and abnormal tumor images. The segmentation of tumor region is performed by the global thresholding with area morphological function method. This proposed method of fully automated classification and segmentation of brain tumor preserves the spatial invariance and inheritance. Furthermore, based on its feature attributes the proposed CNN Deep net classifier, classifies the detected tumor image belongs either to (low grade)benign or (high grade)malignant. This proposed CNN Deep net classification methodology approach with grading system is evaluated both quantitatively and qualitatively. The quantitative measures such as sensitivity, specificity, accuracy, Dice similarity coefficient, precision, F-score of the proposed classification and segmentation methodology states a better segmentation accuracy and classification rate of 99.4% and 99.5% with respect to ground truth images.
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