Brain Tumor Detection Using Deep Learning Neural Network for Medical Internet of Things Applications

Abstract

The Internet of Things technology in medical applications ensures that the medical industry can improve the quality and optimization of medical care. In a medical image, the parameters of the imaging device can be identified and then the medical image is analyzed, and corrective actions can be taken in real time. Digitization has reached many areas of medical technology. Magnetic Resonance Imaging (MRI) is technology development in the medical field that MRI Images can be brain tumor classified as a disease of a patient's internal organs, and then generate high-resolution images for detection. A condition can be identified as a brain tumor by reading the MRI image. MRI technology is useful for detecting brain tumor diseases with drugs. The weakness of detecting brain tumor diseases performed by doctors on MRI images is still manual. The proposed Deep Leamer's Neural Network algorithms are important methods in medical imaging using MRI images for predicting the early symptoms of a disease medical imaging MRI methods. Medical image in human brain tumor position, different functions based on the tumor image are obtained, including contrast, energy, dissimilarity, uniformity, entropy and correlation. The simulation results show that the proposed Deep Learning Neural Network (DLNN) algorithm achieves better detection of abnormal. It is in low grayscale exfoliation and normal tissues in the human brain. The proposed DLNN algorithm also detects human brain tumors within seconds in a short time compared to other algorithms.