Deep residual architectures and ensemble learning for efficient brain tumour classification

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AbstractThe prompt and accurate detection of brain tumours is essential for disease management and life‐saving. This paper introduces an efficient and robust completely automated system for classifying the three prominent types of brain tumour. The aim is to contribute for enhanced classification accuracy with minimum pre‐processing and less inference time. The power of deep networks is thoroughly investigated, with and without transfer learning. Fine‐tuned deep Residual Networks (ResNets) with depth up to 101 are introduced to manage the complex nature of brain images, and to capture their microstructural information. The proposed residual architectures with their in‐depth representations are evaluated and compared to other fine‐tuned networks (AlexNet, GoogLeNet and VGG16). A novel Convolutional Network (ConvNet) built and trained from scratch is also proposed for tumour type classification. Proven models are integrated by combining their decisions using majority voting to obtain the final classification accuracy. Results show that the residual architectures can be optimized efficiently, and a noticeable accuracy can be gained with them. Although ResNet models are deeper than VGG16, they show lower complexity. Results also indicate that building ensemble of models is a successful strategy to enhance the system performance. Each model in the ensemble learns specific patterns with certain filters. This stochastic nature boosts the classification accuracy. The accuracies obtained from ResNet18, ResNet101, and the proposed ConvNet are 98.91%, 97.39% and 95.43%, respectively. The accuracy based on decision fusion for the three networks is 99.57%, which is better than those of all state‐of‐the‐art techniques. The accuracy obtained with ResNet50 is 98.26%, and its fusion with ResNet18 and the designed network yields a 99.35% accuracy, which is also better than those of previous methods, meanwhile achieving minimum detection time requirements. Finally, visual representation of the learned features is provided to understand what the models have learned.