Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning
Abstract The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing survival rate and providing appropriate treatmen...
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00512-6 |
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| author | Sandhya Mekala Phani Kumar S |
| author_facet | Sandhya Mekala Phani Kumar S |
| author_sort | Sandhya Mekala |
| collection | DOAJ |
| description | Abstract The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing survival rate and providing appropriate treatment. Thus, an efficient Secretary Wolf Bird Optimization (SeWBO)_Efficient DenseNet is presented for pancreatic tumor detection using Computed Tomography (CT) scans. Firstly, the input pancreatic CT image is accumulated from a database and subjected to image preprocessing using a bilateral filter. After this, lesion is segmented by utilizing Parallel Reverse Attention Network (PraNet), and hyperparameters of PraNet are enhanced by using the proposed SeWBO. The SeWBO is designed by incorporating Wolf Bird Optimization (WBO) and the Secretary Bird Optimization Algorithm (SBOA). Then, features like Complete Local Binary Pattern (CLBP) with Discrete Wavelet Transformation (DWT), statistical features, and Shape Local Binary Texture (SLBT) are extracted. Finally, pancreatic tumor detection is performed by SeWBO_Efficient DenseNet. Here, Efficient DenseNet is developed by combining EfficientNet and DenseNet. Moreover, the proposed SeWBO_Efficient DenseNet achieves better True Negative Rate (TNR), accuracy, and True Positive Rate (TPR), of 93.596%, 94.635%, and 92.579%. |
| format | Article |
| id | doaj-art-4cc494a8e9ee40ce8c15ff9146403c0e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4cc494a8e9ee40ce8c15ff9146403c0e2025-08-20T02:30:46ZengNature PortfolioScientific Reports2045-23222025-06-0115112710.1038/s41598-025-00512-6Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learningSandhya Mekala0Phani Kumar S1GITAM School of Technology, GITAM Deemed To Be UniversityGITAM School of Technology, GITAM Deemed To Be UniversityAbstract The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing survival rate and providing appropriate treatment. Thus, an efficient Secretary Wolf Bird Optimization (SeWBO)_Efficient DenseNet is presented for pancreatic tumor detection using Computed Tomography (CT) scans. Firstly, the input pancreatic CT image is accumulated from a database and subjected to image preprocessing using a bilateral filter. After this, lesion is segmented by utilizing Parallel Reverse Attention Network (PraNet), and hyperparameters of PraNet are enhanced by using the proposed SeWBO. The SeWBO is designed by incorporating Wolf Bird Optimization (WBO) and the Secretary Bird Optimization Algorithm (SBOA). Then, features like Complete Local Binary Pattern (CLBP) with Discrete Wavelet Transformation (DWT), statistical features, and Shape Local Binary Texture (SLBT) are extracted. Finally, pancreatic tumor detection is performed by SeWBO_Efficient DenseNet. Here, Efficient DenseNet is developed by combining EfficientNet and DenseNet. Moreover, the proposed SeWBO_Efficient DenseNet achieves better True Negative Rate (TNR), accuracy, and True Positive Rate (TPR), of 93.596%, 94.635%, and 92.579%.https://doi.org/10.1038/s41598-025-00512-6Pancreatic tumorDetectionClassificationComputed Tomography imagesDeep Learning |
| spellingShingle | Sandhya Mekala Phani Kumar S Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning Scientific Reports Pancreatic tumor Detection Classification Computed Tomography images Deep Learning |
| title | Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning |
| title_full | Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning |
| title_fullStr | Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning |
| title_full_unstemmed | Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning |
| title_short | Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning |
| title_sort | enhancing pancreatic cancer detection in ct images through secretary wolf bird optimization and deep learning |
| topic | Pancreatic tumor Detection Classification Computed Tomography images Deep Learning |
| url | https://doi.org/10.1038/s41598-025-00512-6 |
| work_keys_str_mv | AT sandhyamekala enhancingpancreaticcancerdetectioninctimagesthroughsecretarywolfbirdoptimizationanddeeplearning AT phanikumars enhancingpancreaticcancerdetectioninctimagesthroughsecretarywolfbirdoptimizationanddeeplearning |