Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach
In recent times, appropriate diagnosis of brain tumour is a crucial task in medical system. Therefore, identification of a potential brain tumour is challenging owing to the complex behaviour and structure of the human brain. To address this issue, a deep learning-driven framework consisting of four...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-05-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251333195 |
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| author | Thota Rishik Sai Santhosh Sachi Nandan Mohanty Nihar Ranjan Pradhan Tauseef Khan Morched Derbali |
| author_facet | Thota Rishik Sai Santhosh Sachi Nandan Mohanty Nihar Ranjan Pradhan Tauseef Khan Morched Derbali |
| author_sort | Thota Rishik Sai Santhosh |
| collection | DOAJ |
| description | In recent times, appropriate diagnosis of brain tumour is a crucial task in medical system. Therefore, identification of a potential brain tumour is challenging owing to the complex behaviour and structure of the human brain. To address this issue, a deep learning-driven framework consisting of four pre-trained models viz DenseNet169, VGG-19, Xception, and EfficientNetV2B2 is developed to classify potential brain tumours from medical resonance images. At first, the deep learning models are trained and fine-tuned on the training dataset, obtained validation scores of trained models are considered as model-wise weights. Then, trained models are subsequently evaluated on the test dataset to generate model-specific predictions. In the weight-aware decision module, the class-bucket of a probable output class is updated with the weights of deep models when their predictions match the class. Finally, the bucket with the highest aggregated value is selected as the final output class for the input image. A novel weight-aware decision mechanism is a key feature of this framework, which effectively deals tie situations in multi-class classification compared to conventional majority-based techniques. The developed framework has obtained promising results of 98.7%, 97.52%, and 94.94% accuracy on three different datasets. The entire framework is seamlessly integrated into an end-to-end web-application for user convenience. The source code, dataset and other particulars are publicly released at https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app [Rishik Sai Santhosh, “Brain Tumour Image Classification Application,” https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app ] for academic, research and other non-commercial usage. |
| format | Article |
| id | doaj-art-ec9465c7a14c43988fa5e87f2ee76f6d |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-ec9465c7a14c43988fa5e87f2ee76f6d2025-08-20T03:09:59ZengSAGE PublishingDigital Health2055-20762025-05-011110.1177/20552076251333195Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approachThota Rishik Sai Santhosh0Sachi Nandan Mohanty1Nihar Ranjan Pradhan2Tauseef Khan3Morched Derbali4 School of Computer Science and Engineering (SCOPE), , Inavolu, Amaravati, Andhra Pradesh, India School of Computer Science and Engineering (SCOPE), , Inavolu, Amaravati, Andhra Pradesh, India School of Computer Science and Engineering (SCOPE), , Inavolu, Amaravati, Andhra Pradesh, India School of Computer Science and Engineering (SCOPE), , Inavolu, Amaravati, Andhra Pradesh, India Faculty of Computing and Information Technology (FCIT), , Jeddah, Saudi ArabiaIn recent times, appropriate diagnosis of brain tumour is a crucial task in medical system. Therefore, identification of a potential brain tumour is challenging owing to the complex behaviour and structure of the human brain. To address this issue, a deep learning-driven framework consisting of four pre-trained models viz DenseNet169, VGG-19, Xception, and EfficientNetV2B2 is developed to classify potential brain tumours from medical resonance images. At first, the deep learning models are trained and fine-tuned on the training dataset, obtained validation scores of trained models are considered as model-wise weights. Then, trained models are subsequently evaluated on the test dataset to generate model-specific predictions. In the weight-aware decision module, the class-bucket of a probable output class is updated with the weights of deep models when their predictions match the class. Finally, the bucket with the highest aggregated value is selected as the final output class for the input image. A novel weight-aware decision mechanism is a key feature of this framework, which effectively deals tie situations in multi-class classification compared to conventional majority-based techniques. The developed framework has obtained promising results of 98.7%, 97.52%, and 94.94% accuracy on three different datasets. The entire framework is seamlessly integrated into an end-to-end web-application for user convenience. The source code, dataset and other particulars are publicly released at https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app [Rishik Sai Santhosh, “Brain Tumour Image Classification Application,” https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app ] for academic, research and other non-commercial usage.https://doi.org/10.1177/20552076251333195 |
| spellingShingle | Thota Rishik Sai Santhosh Sachi Nandan Mohanty Nihar Ranjan Pradhan Tauseef Khan Morched Derbali Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach Digital Health |
| title | Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach |
| title_full | Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach |
| title_fullStr | Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach |
| title_full_unstemmed | Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach |
| title_short | Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach |
| title_sort | neurovision a deep learning driven web application for brain tumour detection using weight aware decision approach |
| url | https://doi.org/10.1177/20552076251333195 |
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