What is it about?
computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis. In this paper, we study deep convolutional neural network (CNN) based automatic analysis of brain glioma tissue images. Our model can obtain high classification accuracy of various stages of the glioma brain tumor.
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Why is it important?
Gliomas are serious form of brain tumor with severe consequences for the patients. Accurately classifying the stages of the disease from histopathological imaging data is challenging and our paper shows the importance of applying AI driven models. We showed that the deep CNNs could extract significant features from the glioma histopathology images with high accuracy.
Perspectives
In this paper, we discussed an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. We used deep convolutional neural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is very promising and with the availability of large scale histopathological image data the deep CNNs are well suited in tackling this challenging problem.
Surya Prasath
Cincinnati Children's Hospital Medical Center
Read the Original
This page is a summary of: Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network, Biomedical Engineering Letters, June 2018, Springer Science + Business Media,
DOI: 10.1007/s13534-018-0077-0.
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