M. Muthalakshmi1,*, T. Merlin Inbamalar2, C. Chandravathi3, K. Saravanan4
Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449
Abstract 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… More >
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