ArticleNeural computing & applications2022
Enhanced lung image segmentation using deep learning.
Article in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 162 citations in OpenAlex.
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- Explainable AI for Image-Based Auxiliary Assessment of Radiation Pneumonitis on Post-Treatment CT Images Using GAN Grad-CAM and Ensemble Learning Techniques.Life (Basel, Switzerland) · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- A method for magnetic resonance splenomegaly image segmentation based on large-kernel multi-scale attention mechanism.Scientific reports · 2026Article
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
- Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs.Scientific reports · 2025Article
- AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.Scientific reports · 2025Article
- Location-specific pathology analysis of monopodial airways in a rabbit model of bronchopulmonary dysplasia: a proof of principle study.BMC medical imaging · 2025Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- Lung image segmentation with improved U-Net, V-Net and Seg-Net techniques.PeerJ. Computer science · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
- A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images.Scientific reports · 2023Article
- A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Article
- Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features.Diagnostics (Basel, Switzerland) · 2023Article
- NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advances in technology, assistive medical systems are emerging with rapid growth and helping healthcare professionals. The proactive diagnosis of diseases with artificial intelligence (AI) and its aligned technologies has been an exciting research area in the last decade. Doctors usually detect tuberculosis (TB) by checking the lungs' X-rays. Classification using deep learning algorithms is successfully able to achieve accuracy almost similar to a doctor in detecting TB. It is found that the probability of detecting TB increases if classification algorithms are implemented on segmented lungs instead of the whole X-ray. The paper's novelty lies in detailed analysis and discussion of U-Net + + results and implementation of U-Net + + in lung segmentation using X-ray. A thorough comparison of U-Net + + with three other benchmark segmentation architectures and segmentation in diagnosing TB or other pulmonary lung diseases is also made in this paper. To the best of our knowledge, no prior research tried to implement U-Net + + for lung segmentation. Most of the papers did not even use segmentation before classification, which causes data leakage. Very few used segmentations before classification, but they only used U-Net, which U-Net + + can easily replace because accuracy and mean_iou of U-Net + + are greater than U-Net accuracy and mean_iou , discussed in results, which can minimize data leakage. The authors achieved more than 98% lung segmentation accuracy and mean_iou 0.95 using U-Net + + , and the efficacy of such comparative analysis is validated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.