AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction - Mittal, Ansh; Kumar, Deepika (EAI Endorsed Transactions on Pervasive Health and Technology, 2019)
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