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 fro...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 images.First,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 features.The introduction of pooling layers in the architecture reduces the feature dimension.The 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 classifier.The 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 normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.展开更多
Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,b...Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,back pain is one of the essential symptoms,but it does not have a specific symptom to recognize at the earlier stage.Numerous significant research studies have been conducted to improve spine tumor recognition accuracy.Nevertheless,the traditional systems are consuming high time to extract the specific region and features.Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.Consequently,in this work,Super-pixel analytics Numerical Characteristics Disintegration Model(SNCDM)is used to segment the tumor affected region.Estimating the super-pix-els of the affected region by this method reduces the variance between the iden-tified pixels.Further,the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features.Derived super-pixels improve the network learning and training process,which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.展开更多
文摘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 images.First,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 features.The introduction of pooling layers in the architecture reduces the feature dimension.The 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 classifier.The 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 normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.
文摘Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,back pain is one of the essential symptoms,but it does not have a specific symptom to recognize at the earlier stage.Numerous significant research studies have been conducted to improve spine tumor recognition accuracy.Nevertheless,the traditional systems are consuming high time to extract the specific region and features.Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.Consequently,in this work,Super-pixel analytics Numerical Characteristics Disintegration Model(SNCDM)is used to segment the tumor affected region.Estimating the super-pix-els of the affected region by this method reduces the variance between the iden-tified pixels.Further,the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features.Derived super-pixels improve the network learning and training process,which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.
