Hybrid Deep Maxout-VGG-16 model for brain tumour detection and classification using MRI images

Abstract

Brain tumor detection is essential to identify tumors at an early stage, allowing for more effective treatment. The patient's chances of recovery and survival can be improved by early detection. The existing methods for detecting brain tumour have several limitations, including limited accessibility, exposure to radiation, high costs and potential for false negatives. To overcome the issues, a Deep Maxout-Visual Geometry Group-16 (DM-VGG-16) model is devised for detecting tumour in brain from Magnetic Resonance Imaging (MRI). Initially, MRI image is sent for pre-processing as input. Here, Non-Local Mean (NLM) filter performs pre-processing. The pre-processed image is subjected to segmentation stage, which is accomplished by Template–based K-means and improved Fuzzy C Means algorithm (TKFCM). Moreover, in feature extraction stage, various features, like area, cluster prominence, Hybrid PCA- Normalized GIST (NGIST) and Improved Median binary Pattern (IMBP) are extracted. Lastly, proposed DM-VGG-16 model is utilized for detection of brain tumors from extracted features. The DM-VGG-16 is the integration of Deep Maxout Network (DMN) and Visual Geometry Group-16 (VGG-16). The DM-VGG-16 outperformed superior results than conventional techniques with performance metrics, including accuracy, True Negative Rate (TNR) and True Positive Rate (TPR) of 90.76 %, 90.65 % and 90.75 % correspondingly.