Gurunathan Akila, Krishnan Batri
Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India.
Brain Imaging Behav. 2022 Jun;16(3):1410-1427. doi: 10.1007/s11682-021-00598-2. Epub 2022 Jan 19.
A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occurring brain tumor in adults. The proposed CNN Deep Net classifier extracts the features internally from the enhanced image and classifies them into normal and abnormal tumor images. The segmentation of tumor region is performed by global thresholding along with an area morphological function. 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 either as (low grade) benign or (high grade) malignant. This proposed CNN Deep net classification 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 classifier states a better segmentation accuracy and classification rate of 99.4% and 99.5% with respect to ground truth images.
提出了一种基于监督的卷积神经网络（CNN）深度网络分类器，用于采用深度学习方法检测、分类和诊断脑膜瘤脑肿瘤。该方法包括对成人原发性脑肿瘤进行预处理、分类和分割。所提出的CNN深度网络分类器从增强图像中内部提取特征，并将其分类为正常和异常肿瘤图像。肿瘤区域的分割通过全局阈值处理和面积形态学函数来执行。这种全自动的脑肿瘤分类和分割方法保持了空间不变性和继承性。此外，基于其特征属性，所提出的CNN深度网络分类器将检测到的肿瘤图像分类为（低级别）良性或（高级别）恶性。对这种带有分级系统的CNN深度网络分类方法进行了定量和定性评估。所提出分类器的敏感性、特异性、准确性、骰子相似系数、精度、F分数等定量指标表明，相对于真实图像，其分割准确率和分类率分别达到了99.4%和99.5%。
