# Brain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes

arXiv: https://arxiv.org/abs/2001.08844 · HTML: https://mlsanity.com/paper/2001.08844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection](https://mlsanity.com/paper/2404.10026/) (2024)
- [A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring](https://mlsanity.com/paper/2102.03532/) (2021)
- [Multi-cancer detection using a computationally efficient CNN with transfer learning](https://mlsanity.com/paper/2606.22400/) (2026)

## Abstract (arXiv)

Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. In general, brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to a very short expected life if it is diagnosed at higher grade. Based on that, brain tumor grading is a very critical step after detecting the tumor in order to achieve an effective treating plan. In this paper, we used Convolutional Neural Network (CNN) which is one of the most widely used deep learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). The proposed CNN classifier is a powerful tool and its overall performance with accuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while the results for the uncropped lesions are 99% accuracy and 98.52% sensitivity and the results for segmented lesion images are 97.62% for accuracy and 97.40% sensitivity.