Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

Colorectal cancer is the second most frequently diagnosed cancer in women and the third most frequently diagnosed cancer in men. At least 80%-95% of the colorectal cancers are evolved from intestinal polyps. Although colonoscopy is regarded as the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the level of hand-eye coordination and the operator skills. Thus, we are primarily motivated by the need for obtaining an early and accurate diagnosis of polyps in the colonoscopy images. In this paper, we employed the powerful object detection neural network ''Mask R-CNN'' to identify and segment polyps in the colonoscopy images. Also, we proposed an ensemble method to combine the two Mask R-CNN models with different backbone structures (ResNet50 and ResNet101) to enhance the performance. Mask R-CNNs in our model were first trained on COCO dataset, and then finely tuned using intestinal polyp dataset since a large number of annotated colonoscopy images are not easily accessible. In order to evaluate our proposed model, we used three open intestinal polyp datasets, CVC-ClinicDB, ETIS-Larib, and CVC-ColonDB. Our results show that our transfer learning-based ensemble model significantly outperforms state-of-the-art methods.

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