SNDT WOMEN'S UNIVERSITY
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Improved Multiclass Lung Disease Classification Using Segmentation and Deep Learning from Chest X-Ray Images (Record no. 133181)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01786nam a2200157 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251104b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Jyoti Singhai |
| 245 ## - TITLE STATEMENT | |
| Title | Improved Multiclass Lung Disease Classification Using Segmentation and Deep Learning from Chest X-Ray Images |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | Pages 318-331 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | Chest X-Ray (CXR) imaging has developed as an important technique for identifying lung diseases, especially in low- and middle-income nations where tuberculosis and pneumonia are serious health problems. With the onset of the COVID-19 pandemic, the need for early and accurate diagnosis has become even more pressing. This research presents a hybrid segmentation and classification for the multiclass lung disease classification using CXR images. The authors use Deep Atrous Attention U-Net (DAA-UNet), specifically designed for lung segmentation, enhancing the Region of Interest (RoI) for classification. The segmented lung regions are then classified using fine-tuned transfer learning on pre-trained models (ResNet101, ChexNet, DenseNet201, and InceptionV3). This hybrid segmentation and classification method achieves an average accuracy of 96.87%, significantly outperforming other classification models, as evidenced by metrics such as precision, sensitivity, specificity, and F1-score. This method exemplifies the potential for integrating deep learning classifiers with image segmentation to improve the diagnosis of lung disease, enabling early intervention and improved patient outcomes. |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="ASPP U-Net">ASPP U-Net</a> |
| -- | <a href="Attention U-Net">Attention U-Net</a> |
| -- | <a href="Chest X-Ray">Chest X-Ray</a> |
| -- | <a href="Lung segmentation">Lung segmentation</a> |
| -- | <a href="Lung disease classifications">Lung disease classifications</a> |
| -- | <a href="U-Net">U-Net</a> |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vivek Kumar Yadav |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 80270 |
| Host Itemnumber | 114207 |
| Place, publisher, and date of publication | New Delhi IETE |
| Title | IETE Technical Review |
| International Standard Serial Number | 0256-4602 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 04/11/2025 | JP980.1 | 04/11/2025 | 04/11/2025 | Journal Article |