Deep Learning Framework for the Prediction of Childhood Medulloblastoma

作     者：M.Muthalakshmi T.Merlin Inbamalar C.Chandravathi K.Saravanan 

作者机构：Department of Biomedical EngineeringVeltech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electronics and Instrumentation EngineeringSaveetha Engineering CollegeChennaiTamil NaduIndia Department of Information TechnologyJ.J.College of Engineering and TechnologyTrichyTamil NaduIndia Department of Information TechnologyR.M.D Engineering CollegeKavaraipettaiChennaiTamil NaduIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第7期

页      面：735-747页

核心收录：

学科分类：1002[医学-临床医学] 08[工学] 100214[医学-肿瘤学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：Brain tumour childhood medulloblastoma deep learning histopathological images medical image analysis 

摘      要：This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological ***,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep *** introduction of pooling layers in the architecture reduces the feature *** extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network *** performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and *** results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.