# Predictive modeling of brain tumor: A Deep learning approach

arXiv: https://arxiv.org/abs/1911.02265 · HTML: https://mlsanity.com/paper/1911.02265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data](https://mlsanity.com/paper/2301.11468/) (2023)
- [Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification](https://mlsanity.com/paper/2507.12177/) (2025)
- [Multi-cancer detection using a computationally efficient CNN with transfer learning](https://mlsanity.com/paper/2606.22400/) (2026)
- [Advances of Artificial Intelligence in Classical and Novel Spectroscopy-Based Approaches for Cancer Diagnostics. A Review](https://mlsanity.com/paper/2208.04008/) (2022)
- [Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation](https://mlsanity.com/paper/2406.19690/) (2024)
- [Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models](https://mlsanity.com/paper/2501.12488/) (2025)

## Abstract (arXiv)

Image processing concepts can visualize the different anatomy structure of the human body. Recent advancements in the field of deep learning have made it possible to detect the growth of cancerous tissue just by a patient's brain Magnetic Resonance Imaging (MRI) scans. These methods require very high accuracy and meager false negative rates to be of any practical use. This paper presents a Convolutional Neural Network (CNN) based transfer learning approach to classify the brain MRI scans into two classes using three pre-trained models. The performances of these models are compared with each other. Experimental results show that the Resnet-50 model achieves the highest accuracy and least false negative rates as 95% and zero respectively. It is followed by VGG-16 and Inception-V3 model with an accuracy of 90% and 55% respectively.