Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images
For both women and men over 60, liver cancer is the primary cause of cancer-related deaths. To help physicians to diagnose patients more accurately, computer-assisted imaging techniques have become increasingly important in recent years. Recent, deep Convolutional Neural Network (CNN) research has p...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Bulgarian Academy of Sciences
2024-09-01
|
| Series: | International Journal Bioautomation |
| Subjects: | |
| Online Access: | http://www.biomed.bas.bg/bioautomation/2024/vol_28.3/files/28.3_03.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549886358552576 |
|---|---|
| author | Bilga Jacob R. S. Vinod Kumar S. S. Kumar |
| author_facet | Bilga Jacob R. S. Vinod Kumar S. S. Kumar |
| author_sort | Bilga Jacob |
| collection | DOAJ |
| description | For both women and men over 60, liver cancer is the primary cause of cancer-related deaths. To help physicians to diagnose patients more accurately, computer-assisted imaging techniques have become increasingly important in recent years. Recent, deep Convolutional Neural Network (CNN) research has produced amazing improvements in image segmentation and classification. The same issue of diagnosing liver nodules in computed tomography (CT) scans is addressed in this research by introducing a novel Computer-Aided Detection (CAD) system that makes use of an Efficient Network (EfficientNet) image classification algorithm. Unlike CNN, which adjusts its network parameters arbitrarily, a set of predetermined scaling coefficients is used in the EfficientNet scaling technique to reliably scale the network’s breadth, depth, and resolution. Here the EfficientNet models are assessed by varying the input dimensions of the CT scans from The Liver Tumor Segmentation (LiTs) dataset. Finally, the performance evaluation shows that the input dimension 224x224 effectively classified the images and is superior to the other models evaluated with 0.991 AUC and 99.37% F1-Score, precision 99.44%, recall 99.30%, specificity 99.43%, and accuracy 99.36% for Kaggle datasets. |
| format | Article |
| id | doaj-art-b2b64bf8c9d0472288800ea7be217cb3 |
| institution | Kabale University |
| issn | 1314-1902 1314-2321 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Bulgarian Academy of Sciences |
| record_format | Article |
| series | International Journal Bioautomation |
| spelling | doaj-art-b2b64bf8c9d0472288800ea7be217cb32025-01-10T12:57:08ZengBulgarian Academy of SciencesInternational Journal Bioautomation1314-19021314-23212024-09-0128315116010.7546/ijba.2024.28.3.001001Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT ImagesBilga Jacob0R. S. Vinod KumarS. S. KumarDepartment of Electronics and Communication Engineering, Noorul Islam Center for Higher Education, Kumarakoil, Kanyakumari, Tamil Nadu, IndiaFor both women and men over 60, liver cancer is the primary cause of cancer-related deaths. To help physicians to diagnose patients more accurately, computer-assisted imaging techniques have become increasingly important in recent years. Recent, deep Convolutional Neural Network (CNN) research has produced amazing improvements in image segmentation and classification. The same issue of diagnosing liver nodules in computed tomography (CT) scans is addressed in this research by introducing a novel Computer-Aided Detection (CAD) system that makes use of an Efficient Network (EfficientNet) image classification algorithm. Unlike CNN, which adjusts its network parameters arbitrarily, a set of predetermined scaling coefficients is used in the EfficientNet scaling technique to reliably scale the network’s breadth, depth, and resolution. Here the EfficientNet models are assessed by varying the input dimensions of the CT scans from The Liver Tumor Segmentation (LiTs) dataset. Finally, the performance evaluation shows that the input dimension 224x224 effectively classified the images and is superior to the other models evaluated with 0.991 AUC and 99.37% F1-Score, precision 99.44%, recall 99.30%, specificity 99.43%, and accuracy 99.36% for Kaggle datasets.http://www.biomed.bas.bg/bioautomation/2024/vol_28.3/files/28.3_03.pdfdeep learningconvolution neural networkcomputed tomographyliver tumor classificationthe liver tumor segmentation |
| spellingShingle | Bilga Jacob R. S. Vinod Kumar S. S. Kumar Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images International Journal Bioautomation deep learning convolution neural network computed tomography liver tumor classification the liver tumor segmentation |
| title | Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images |
| title_full | Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images |
| title_fullStr | Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images |
| title_full_unstemmed | Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images |
| title_short | Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images |
| title_sort | multiple scaling based efficientnet modelling for liver tumor classification on ct images |
| topic | deep learning convolution neural network computed tomography liver tumor classification the liver tumor segmentation |
| url | http://www.biomed.bas.bg/bioautomation/2024/vol_28.3/files/28.3_03.pdf |
| work_keys_str_mv | AT bilgajacob multiplescalingbasedefficientnetmodellingforlivertumorclassificationonctimages AT rsvinodkumar multiplescalingbasedefficientnetmodellingforlivertumorclassificationonctimages AT sskumar multiplescalingbasedefficientnetmodellingforlivertumorclassificationonctimages |