An Adaptive Eroded Deep Convolutional neural network for brain image segmentation and classification using Inception ResnetV2

Abstract

In today's scenario, the main challenging issue in medical field is the tumor detection in human brain. An uncontrolled growth of abnormal nerve tissues contributes to brain tumor. This state of abnormal growth leads to malignant cells which causes a serious issue for its effective treatment. An automated process of brain tumour detection has grabbed attention with improved technological development. This research paper focuses on the effort to segment and identify tumor in human brain. The main steps includes preprocessing, segmentation and classification, where the initial one deals with Anisotropic Diffusion filter followed by Binary based Boundary box detection. The novel procedure of segmentation is done with proposed Adaptive Eroded Deep Convolutional neural network (AEDCNN). It enables to provide distinct segmentation between meningioma, glioma and pituitary brain region. The next step of segmentation is proposed Inception resnetV2, which acts as the novel classification method in brain images. The primary stage of separation is to divide the tumor cell region. The algorithm AEDCNNS determines a degree of spatial membership. The Inception holds different information scales which contributes to input image data. Particularly for classification state mission, we focus on three diseases known as meningioma, glioma and pituitary as benign or malignant. With increase in accuracy, the proposed Inception resnetV2 proves an effective machine learning mechanism for image classification. The accuracy and precision are 97.89%, 93.27% respectively. It proves to be efficient tool for physicians working in medical field.