Accurate classification of brain tumor subtypes is important for prognosis and treatment. In this study, we optimized and applied non-deep learning methods based on hand-crafted features and deep learning methods based on transfer learning using softmax as classification and KNN and SVM as classification for features extracted from deep features of ResNet101. For non-deep learning techniques, we extracted multimodal features as input to machine learning classifiers. For convolutional neural networks, we optimized and applied GoogleNet and ResNet101with transfer learning approach. The performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR), total accuracy (TA), and area under the receiver operating curve (AUC) using Jack-knife 10-fold cross validation (CV) for the testing and validation of the dataset. For two-class classification, entropy features using SVM Gaussian yielded the highest performance with 93.84% TA and 0.9874 AUC, and GoogleNet yielded 99.33% TA. For Multiclass classification, the highest performance to detect pituitary tumor yielded 95.65% accuracy and 0.95 AUC using ResNet101 with transfer learning. Deep features from ResNet101 using KNN improved detection of pituitary tumor (98.80% accuracy, 0.99 AUC), glioma (93.47% accuracy, 0.93 AUC), and meningioma (93.36% accuracy, 0.89 AUC). The deep features ResNet101-SVM to detect pituitary tumor yielded performance (98.
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<p>Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.</p>
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