ArticleDiagnostics (Basel, Switzerland)2023
A Convolutional Neural Network Architecture for Segmentation of Lung Diseases Using Chest X-ray Images.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Benchmarking MedViT and hybrid CNN-ViT architectures for multi-label thoracic disease classification.Scientific reports · 2026Article
- Enhancing lung diseases recognition through CNN-RNN methodologies.Scientific reports · 2026Article
- Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.International journal of biomedical imaging · 2026Article
- Comparative Analysis of Foundational, Advanced, and Traditional Deep Learning Models for Hyperpolarized Gas MRI Lung Segmentation: Robust Performance in Data-Constrained Scenarios.Bioengineering (Basel, Switzerland) · 2025Article
- Real-Time Coronary Artery Dominance Classification from Angiographic Images Using Advanced Deep Video Architectures.Diagnostics (Basel, Switzerland) · 2025Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.Pulmonary medicine · 2025Article
- Attention based UNet model for breast cancer segmentation using BUSI dataset.Scientific reports · 2024Article
- Deep learning based uterine fibroid detection in ultrasound images.BMC medical imaging · 2024Article
- Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor.Scientific reports · 2024Article
- An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder.Life (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
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Abstract
The segmentation of lungs from medical images is a critical step in the diagnosis and treatment of lung diseases. Deep learning techniques have shown great promise in automating this task, eliminating the need for manual annotation by radiologists. In this research, a convolution neural network architecture is proposed for lung segmentation using chest X-ray images. In the proposed model, concatenate block is embedded to learn a series of filters or features used to extract meaningful information from the image. Moreover, a transpose layer is employed in the concatenate block to improve the spatial resolution of feature maps generated by a prior convolutional layer. The proposed model is trained using k-fold validation as it is a powerful and flexible tool for evaluating the performance of deep learning models. The proposed model is evaluated on five different subsets of the data by taking the value of k as 5 to obtain the optimized model to obtain more accurate results. The performance of the proposed model is analyzed for different hyper-parameters such as the batch size as 32, optimizer as Adam and 40 epochs. The dataset used for the segmentation of disease is taken from the Kaggle repository. The various performance parameters such as accuracy, IoU, and dice coefficient are calculated, and the values obtained are 0.97, 0.93, and 0.96, respectively.
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