Abstract
According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor’s malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of ±1% and an area under the receiver operation characteristics curve (Az) of 0.9991 ± 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation ±16% and a mean Az of 0.7896 ± 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.
Highlights
Diffuse gliomas, the most common primary central nervous system (CNS) neoplasm, are formed of tumor cells that display differentiation of glial cells
Classification of tumors of the CNS [1,2], diffuse gliomas are graded according to their malignant
By evaluating the 10-fold differentiating grade 2 gliomas from the others achieved an accuracy of 98.7%, a sensitivity of 96.9%, cross-validation, the transferred deep convolutional neural network (DCNN) achieved a mean accuracy of 97.9% with an SD of ±1% and and a specificity of 99.2%
Summary
The most common primary central nervous system (CNS) neoplasm, are formed of tumor cells that display differentiation of glial cells. Classification of tumors of the CNS [1,2], diffuse gliomas are graded according to their malignant. Patients with diffuse gliomas of lower grades (grades 2 and 3). Glioblastoma multiforme (GBM) is the most aggressive tumor type (WHO grade 4) with dismal prognoses despite advances in various aspects of its clinical management [4]. Since therapeutic strategies for the various grades are not identical [5], distinguishing the different grades of diffuse gliomas is a critical issue in clinical settings. Since the definitions are semiquantitative and subjective [6,7], histopathological analyses sometimes result in ambiguity in glioma grading. Previous reports revealed that the heterogeneous expressions of cellular features may result in misgrading in up to one-third of cases with unguided surgical tissue sampling [7,8,9,10,11]
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