Deep Learning for MRI-Based Brain Tumor Identification and Classification

EasyChair Preprint no. 11544

Abstract

Radiology tumour spotting is complicated and requires medical knowledge. Thus, lack of doctors should not delay cancer detection programmes. Biomedical image processing software helps find brain tumours in MRI data. This study segmented and detected brain tumours using MRI sequence images. This process is complicated by the similarity of normal tissues and the wide range of tumour tissues in different patients. Brain tumour detection is the main goal. Brain tumour diagnosis requires precise tumour size and position. This paper presents a deep learning-based brain tumour MRI segmentation and classification method. We preprocessed the photos with the Gaussian blur filter and image enhancement tool. Binary thresholding segments. Morphological methods reveal traits. CNN will assess brain MRI regularity. This study uses Kaggle Dataset. We train with 255 brain MRIs, 155 with tumours and 98 normal, and achieve 97% accuracy. We test the model using Kaggle brain MR datasets. Brain tumour detection, segmentation, and categorization; medical image processing. This study proposed an easy-to-implement way for identifying and classifying bone tumours.

Keyphrases: Brain Tumor, CNN, feature extraction, MRI imaging, Segmentation

@Booklet{EasyChair:11544,
  author = {D Jareena Begum and Sp. Chokkalingam and B. Sundaravadivazhagan},
  title = {Deep Learning for MRI-Based Brain Tumor Identification and Classification},
  howpublished = {EasyChair Preprint no. 11544},

  year = {EasyChair, 2023}}