| Autor: |
Eid Albalawi, Eali Stephen Neal Joshua, N. M. Joys, Surbhi Bhatia Khan, Hadil Shaiba, Sultan Ahmad, Jabeen Nazeer |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Frontiers in Medicine, Vol 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-858X |
| DOI: |
10.3389/fmed.2024.1429291 |
| Popis: |
IntroductionOur research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning.MethodsOur model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE).ResultsThe evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach.DiscussionOur investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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