Feature Extraction and Classification of Gray-Scale Images of Brain Tumor using Deep Learning

Abstract

Deep Learning using CNN plays a paramount role for the classification methods applied on medical image data. With a crucial role in accurate diagnosis, treatment planning and patient management for medical and healthcare systems, CNNs won accolades in the Deep Learning research. As simple the learning model so precise are the results for decision making. The proposed Sequential model of CNN is built with Parametric ReLU with the values aligned to geometric mean, attains a specific goal of tumor classification. The additional support of ground-truth aid in deciding the shape and severity of tumor in the Grayscale MRI of brain tumor. The simple Sequential model, although a minimal version has proved achieved significant classification goals using the GMP-ReLU. Comparative results with variants of ReLU have been charted in this article standing with the proof of consistent classification model with parametric-ReLU. The proposed design is conducted on images from Kaggle and a model is trained (classifier is built), which can be considered as ideal filter for all the benchmark images. The accuracy of proposed design is considerably improved compared to normal ReLU up to 89.214%.