Research Article
AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction
@ARTICLE{10.4108/eai.12-2-2019.161976, author={Ansh Mittal and Deepika Kumar}, title={AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={5}, number={17}, publisher={EAI}, journal_a={PHAT}, year={2019}, month={2}, keywords={MRI, Machine Learning, Deep Learning, AiCNNs, CNN, Data Augmentation, ImageNet}, doi={10.4108/eai.12-2-2019.161976} }- Ansh Mittal
Deepika Kumar
Year: 2019
AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction
PHAT
EAI
DOI: 10.4108/eai.12-2-2019.161976
Abstract
INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis.
OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (AiCNNs) that accurately classifies brain MRI scans to 3 classes of brain tumor and negative diagnosis results.
METHODS: AiCNNs model integrates 5 already trained models including simple convolutional neural networks (one uses a simple CNN while the other utilizes data augmentation) and three pre-trained networks whose weights are transferred from ImageNet dataset.
RESULTS: AiCNNs model was trained on 3501 augmented T1-weighted contrast enhanced MRI (CE-MRI) brain images. Validation results of 99.49% (loss=0.0303) had been achieved by AiCNNs on a set of 1167 images, which outperform its contemporaries which have got results upto 97.81% (loss=0.1794) and 97.79% (loss=0.1787).
CONCLUSION: AiCNNs has been shown to obtained a test accuracy of 98.89 % on a set of 1167 images
Copyright © 2019 Ansh Mittal et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
