Identification of Brain Tumor Type Using Deep CNN

EasyChair Preprint 7317

Abstract

With the wide application of computer technology, medical health data has also increased dramatically, and data-driven medical big data analysis methods have emerged as the times require, providing assistance for intelligent identification of medical health. Traditional machine learning methods can’t effectively mine the rich information contained in Brain tumor medical big data, while deep learning builds a hierarchical model by simulating the human brain. It is a deep learning method that extracts feature from the bottom to the top level from the original brain tumor medical image data. A data analysis model based on deep learning for brain tumor medical images was constructed and is used for intelligent identification and diagnosis of diseases and also provide recommendation and notification to patients through SMS or email. The model uses massive brain tumor medical big data to select and optimize model parameters, and automatically learns the pathological analysis process of doctors or medical researchers through the model, and finally intelligently conducts disease judgment and effective decision based on the analysis results of brain tumor medical big data. In order to verify the validity of the proposed method for medical image data, this paper selects the brain MRI medical image data set as medical image data.

Keyphrases: Brain Tumor, MRI medical image, deep learning, diagnosis, pathological analysis

@booklet{EasyChair:7317,
  author    = {Saranya Natarajan and S R Sowmiya},
  title     = {Identification of Brain Tumor Type Using Deep CNN},
  howpublished = {EasyChair Preprint 7317},
  year      = {EasyChair, 2022}}