phat 19(17): e5

Research Article

AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction

Download23 downloads
  • @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
Ansh Mittal1, Deepika Kumar1,*
  • 1: Bharati Vidyapeeth’s College of Engineering, New Delhi, India
*Contact email: Deepika.kumar@bharatividyapeeth.edu

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