Eid Albalawi
Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation
Albalawi, Eid; Neal Joshua, Eali Stephen; Joys, N. M.; Bhatia Khan, Surbhi; Shaiba, Hadil; Ahmad, Sultan; Nazeer, Jabeen
Authors
Eali Stephen Neal Joshua
N. M. Joys
Dr Surbhi Khan S.Khan138@salford.ac.uk
Lecturer in Data Science
Hadil Shaiba
Sultan Ahmad
Jabeen Nazeer
Abstract
Introduction: Our 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. Methods: Our 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). Results: The evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach. Discussion: Our 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.
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 26, 2024 |
Online Publication Date | Jul 19, 2024 |
Deposit Date | Sep 12, 2024 |
Publicly Available Date | Sep 12, 2024 |
Journal | Frontiers in Medicine |
Electronic ISSN | 2296-858X |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 11 |
Pages | 1429291 |
DOI | https://doi.org/10.3389/fmed.2024.1429291 |
Keywords | CLAHE, two-parameter logistic type distribution, ROI segmentation, performance evaluation, image segmentation, lung cancer detection |
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