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

EAI Endorsed Transactions on Pervasive Health and Technology

Ansh Mittal¹  Deepika Kumar¹ 

[1] Bharati Vidyapeeth’s College of Engineering, New Delhi, India;

关键词: mri;    machine learning;    deep learning;    aicnns;    cnn;    data augmentation;    imagenet;   

DOI  :  10.4108/eai.12-2-2019.161976

来源: DOAJ

【 摘 要 】

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

【 授权许可】

Unknown