AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction
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 (AiCNN...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2019-02-01
|
| Series: | EAI Endorsed Transactions on Pervasive Health and Technology |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.12-2-2019.161976 |
| _version_ | 1818290830205517824 |
|---|---|
| author | Ansh Mittal Deepika Kumar |
| author_facet | Ansh Mittal Deepika Kumar |
| author_sort | Ansh Mittal |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-12-13T02:34:24Z |
| format | Article |
| id | doaj.art-d61779694bab4e5fbc602ab9a196a094 |
| institution | Directory Open Access Journal |
| issn | 2411-7145 |
| language | English |
| last_indexed | 2024-12-13T02:34:24Z |
| publishDate | 2019-02-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Pervasive Health and Technology |
| spelling | doaj.art-d61779694bab4e5fbc602ab9a196a0942022-12-22T00:02:26ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Pervasive Health and Technology2411-71452019-02-0151710.4108/eai.12-2-2019.161976AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor PredictionAnsh Mittal0Deepika Kumar1Bharati Vidyapeeth’s College of Engineering, New Delhi, IndiaBharati Vidyapeeth’s College of Engineering, New Delhi, IndiaINTRODUCTION: 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 imageshttps://eudl.eu/pdf/10.4108/eai.12-2-2019.161976mrimachine learningdeep learningaicnnscnndata augmentationimagenet |
| spellingShingle | Ansh Mittal Deepika Kumar AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction EAI Endorsed Transactions on Pervasive Health and Technology mri machine learning deep learning aicnns cnn data augmentation imagenet |
| title | AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction |
| title_full | AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction |
| title_fullStr | AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction |
| title_full_unstemmed | AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction |
| title_short | AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction |
| title_sort | aicnns artificially integrated convolutional neural networks for brain tumor prediction |
| topic | mri machine learning deep learning aicnns cnn data augmentation imagenet |
| url | https://eudl.eu/pdf/10.4108/eai.12-2-2019.161976 |
| work_keys_str_mv | AT anshmittal aicnnsartificiallyintegratedconvolutionalneuralnetworksforbraintumorprediction AT deepikakumar aicnnsartificiallyintegratedconvolutionalneuralnetworksforbraintumorprediction |