Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.

The biopsy is a gold standard method for tumor grading. However, due to its invasive nature, it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer-aided diagnosis (CAD) tool is required. Recently, many magnetic resonance imaging (MRI)-based CAD tools have been proposed for brain tumor grading. The MRI has several sequences, which can express tumor structure in different ways. However, a suitable MRI sequence for brain tumor classification is not yet known. The most common brain tumor is 'glioma', which is the most fatal form. Therefore, in the proposed study, to maximize the classification ability between low-grade versus high-grade glioma, three datasets were designed comprising three MRI sequences: T1-Weighted (T1W), T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Further, five well-established convolutional neural networks, AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 were adopted for tumor classification. An ensemble algorithm was proposed using the majority vote of above five deep learning (DL) models to produce more consistent and improved results than any individual model. Five-fold cross validation (K5-CV) protocol was adopted for training and testing. For the proposed ensembled classifier with K5-CV, the highest test accuracies of 98.88 ± 0.63%, 97.98 ± 0.86%, and 94.75 ± 0.61% were achieved for FLAIR, T2W, and T1W-MRI data, respectively. FLAIR-MRI data was found to be most significant for brain tumor classification, where it showed a 4.17% and 0.91% improvement in accuracy against the T1W-MRI and T2W-MRI sequence data, respectively. The proposed ensembled algorithm (MajVot) showed significant improvements in the average accuracy of three datasets of 3.60%, 2.84%, 1.64%, 4.27%, and 1.14%, respectively, against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50.

Clinicopathological study of thymomas--correlation of histologic subtype to myasthenia gravis and prognosis.

Thymoma is the most common primary tumor of anterior superior mediastinum. Sixty cases of thymomas over a 12 year period were analysed and the histologic subtype, according to Marino and Muller-Hermilink, classification was correlated with presence or absence of myasthenia gravis (MG) and capsular invasion. Thirty four patients had myasthenia gravis associated with thymoma and there was one case of pure red cell aplasia. There were 3 (1) predominantly cortical, 28 (20) cortical, 12 (9) mixed, 16 (4) medullary thymomas and 1 (0) thymic carcinoma (Figures in parenthesis indicate number of cases associated with MG). Capsular invasion was seen in 25 cases. Association with myasthenia gravis and capsular invasion were seen predominantly in cortical and mixed thymomas which were also associated with aggressive behaviour.