Multiple classification of brain tumor images using a new and efficient convolutional neural network-based model

In conventional methods, the detection of tumour disease from brain images using magnetic resonance imaging is a difficult and human error-prone field of study that requires an expert medical doctor. Incomplete or inaccurate detection of brain tumours can have significant undesirable consequences such as shortening of human life. In order to overcome these difficulties, many researchers are working on autonomous disease detection supported by artificial intelligence. The aim of this study is to utilise brain magnetic resonance images with deep learning architectures for fast and reliable autonomous cancer detection. In this study, brain images are classified using two different datasets and a convolutional neural network infrastructure, which are widely used in the publicly available literature. The results obtained as a result of experiments with similar parameters in training, validation and testing processes are compared in detail with other studies in the literature and the differences between them are presented. The new convolutional neural network-based model proposed in the study achieved 99.76% classification result in the accuracy evaluation metric. The results obtained showed that the model proposed in the study can be used with high accuracy in brain tumour detection and can shed light on other fields of study.