Brain MR Image Classification Based on Deep Features by Using Extreme Learning Machines
Ali Arı · Biomedical Journal of Scientific & Technical Research · 2020
Magnetic Resonance Imaging (MRI) is a noninvasive medical testing procedure that can help physicians to examine internal body structures and diagnose a variety of disorders, such as tumors.MRI has some advantages over other imaging methods: mainly that there is no risk of being exposed to radiation.As a result of this many researchers from the community of computer vision and machine learning are interested in classifying or segmenting MR images to help physicians perform more detailed investigations and an automatic system for brain tumor detection and classification was proposed.Firstly, brain MR images are preprocessed by using a 5x5 Gaussian filter.Secondly, deep feature extraction was performed by using Alex Net and VGG16 models of pre-trained Convolutional Neural Network (CNN).The obtained feature vectors are combined.These feature vectors were used for MR images classification by Extreme Learning Machines (ELM) classifier.The performances of the proposed methods have been evaluated on three different data sets.Performance parameters used to assess the results are; accuracy, sensitivity, selectivity and Jaccard's similarity index for tumor detection.The experimental results showed that the proposed system is superior in detecting and classifying brain tumors when compared with other systems.