Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation
Abstract Bladder cancer diagnosis is a challenging task because of its intricacy and variation of tumor features. Moreover, morphological similarities of the cancerous cells make manual diagnosis time-consuming. Recently, machine learning and deep learning methods have been utilized to diagnose blad...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02767-5 |
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| author | Poonam Sharma Bhisham Sharma Dhirendra Prasad Yadav Deepti Thakral Julian L. Webber |
| author_facet | Poonam Sharma Bhisham Sharma Dhirendra Prasad Yadav Deepti Thakral Julian L. Webber |
| author_sort | Poonam Sharma |
| collection | DOAJ |
| description | Abstract Bladder cancer diagnosis is a challenging task because of its intricacy and variation of tumor features. Moreover, morphological similarities of the cancerous cells make manual diagnosis time-consuming. Recently, machine learning and deep learning methods have been utilized to diagnose bladder cancer. However, manual feature requirements for machine learning and the high volume of data for deep learning make them less reliable for real-time application. This study developed a hybrid model using CNN (Convolutional Neural Network) and less attention-based ViT (Vision Transformer) for bladder lesion diagnosis. Our hybrid model contains two blocks of the inceptionV3 to extract spatial features. Furthermore, the global co-relation of the features is achieved using hybrid attention modules incorporated in the ViT encoder. The experimental evaluation of the model on a dataset consisting of 17,540 endoscopic images achieved an average accuracy, precision and F1-score of 97.73%, 97.21% and 96.86%, respectively, using a 5-fold cross-validation strategy. We compared the results of the proposed method with CNN and ViT-based methods under the same experimental condition, and we achieved much better performance than our counterparts. |
| format | Article |
| id | doaj-art-94425f4551684df7b8a41754f3c30fd5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-94425f4551684df7b8a41754f3c30fd52025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-02767-5Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformationPoonam Sharma0Bhisham Sharma1Dhirendra Prasad Yadav2Deepti Thakral3Julian L. Webber4Chitkara University Institute of Engineering and Technology, Chitkara UniversityCentre of Research Impact and Outcome, Chitkara UniversityDepartment of Computer Engineering & Applications, G.L.A. UniversityDepartment of Computer Science and Technology, Manav Rachna UniversityDepartment of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST)Abstract Bladder cancer diagnosis is a challenging task because of its intricacy and variation of tumor features. Moreover, morphological similarities of the cancerous cells make manual diagnosis time-consuming. Recently, machine learning and deep learning methods have been utilized to diagnose bladder cancer. However, manual feature requirements for machine learning and the high volume of data for deep learning make them less reliable for real-time application. This study developed a hybrid model using CNN (Convolutional Neural Network) and less attention-based ViT (Vision Transformer) for bladder lesion diagnosis. Our hybrid model contains two blocks of the inceptionV3 to extract spatial features. Furthermore, the global co-relation of the features is achieved using hybrid attention modules incorporated in the ViT encoder. The experimental evaluation of the model on a dataset consisting of 17,540 endoscopic images achieved an average accuracy, precision and F1-score of 97.73%, 97.21% and 96.86%, respectively, using a 5-fold cross-validation strategy. We compared the results of the proposed method with CNN and ViT-based methods under the same experimental condition, and we achieved much better performance than our counterparts.https://doi.org/10.1038/s41598-025-02767-5Bladder CancerDeep learningTransformer encoderSelf-AttentionHybrid attentionVision transformer |
| spellingShingle | Poonam Sharma Bhisham Sharma Dhirendra Prasad Yadav Deepti Thakral Julian L. Webber Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation Scientific Reports Bladder Cancer Deep learning Transformer encoder Self-Attention Hybrid attention Vision transformer |
| title | Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation |
| title_full | Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation |
| title_fullStr | Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation |
| title_full_unstemmed | Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation |
| title_short | Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation |
| title_sort | bladder lesion detection using efficientnet and hybrid attention transformer through attention transformation |
| topic | Bladder Cancer Deep learning Transformer encoder Self-Attention Hybrid attention Vision transformer |
| url | https://doi.org/10.1038/s41598-025-02767-5 |
| work_keys_str_mv | AT poonamsharma bladderlesiondetectionusingefficientnetandhybridattentiontransformerthroughattentiontransformation AT bhishamsharma bladderlesiondetectionusingefficientnetandhybridattentiontransformerthroughattentiontransformation AT dhirendraprasadyadav bladderlesiondetectionusingefficientnetandhybridattentiontransformerthroughattentiontransformation AT deeptithakral bladderlesiondetectionusingefficientnetandhybridattentiontransformerthroughattentiontransformation AT julianlwebber bladderlesiondetectionusingefficientnetandhybridattentiontransformerthroughattentiontransformation |