An intelligent driven deep residual learning framework for brain tumor classification using MRI images

Brain tumor classification is an expensive complicated challenge in the sector of clinical image analysis. Machine learning algorithms enabled radiologists to accurately diagnose tumors without requiring major surgery. However, several challenges rise; first, the major challenge in designing the most accurate deep learning architecture for classifying brain tumors; and secondly, difficulty of finding an expert who is experienced in the field of classifying brain tumors using images by deep learning models. These difficulties made us motivated to propose an advanced and high accurate framework based on the concepts of deep learning and evolutionary algorithms to automatically design the ResNet architecture efficiently for classifying three types of brain tumors on a large database of MRI images. Thus, we propose an optimization-based deep convolutional ResNet model combined with a novel evolutionary algorithm to optimize the architecture and hyperparameters of deep ResNet model automatically without need of human experts as well manual architecture design which is complicated task to classify different types of brain tumor. Also, we propose an improved version of ant colony optimization (IACO) based on the concepts of differential evolution strategy and multi-population operators. These two concepts make an effective balance for solution diversity and convergence speed as well as enhancing the optimization performance and avoiding falling into the local optima for designing the deep learning-based ResNet architectures. The experimental finding revealed that our proposed framework obtained an average accuracy of 0.98694 which efficiently shows that our IACO-ResNet algorithm can help excellently with the automatic classification of brain tumors.