Improving Biomedical Image Pattern Identification by Deep B4‐GraftingNet: Application to Pneumonia Detection DOI Creative Commons
Shishir Shah, Syed Taimoor Hussain Shah, Abdul Muiz Fayyaz

et al.

IET Image Processing, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT VGG‐16 and Inception are widely used CNN architectures for image classification, but they face challenges in target categorization. This study introduces B4‐GraftingNet, a novel deep learning model that integrates VGG‐16's hierarchical feature extraction with Inception's diversified receptive field strategy. The is trained on the OCT‐CXR dataset evaluated NIH‐CXR to ensure robust generalization. Unlike conventional approaches, B4‐GraftingNet incorporates binary particle swarm optimization (BPSO) selection grad‐CAM interpretability. Additionally, performed, multiple machine classifiers (SVM, KNN, random forest, naïve Bayes) determine optimal representation. achieves 94.01% accuracy, 94.22% sensitivity, 93.36% specificity, 95.18% F1‐score maintains 87.34% accuracy despite not being it. These results highlight model's superior classification performance, adaptability, potential real‐world deployment both medical general tasks.

Language: Английский

SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification DOI Creative Commons

Qurat-ul-ain Chaudhary,

Shahzad Ahmad Qureshi,

Touseef Sadiq

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104025 - 104025

Published: Jan. 1, 2025

Language: Английский

Citations

3
Application of Artificial Intelligence in Radiological Image Analysis for Pulmonary Disease Diagnosis: A Review of Current Methods and Challenges DOI Creative Commons
Karolina Zalewa, Joanna Olszak, Wojciech Kapłan

et al.

Journal of Education Health and Sport, Journal Year: 2025, Volume and Issue: 77, P. 56893 - 56893

Published: Jan. 14, 2025

Introduction and purposeArtificial intelligence (AI), particularly machine learning (ML) deep (DL), is revolutionizing radiology by improving diagnostic accuracy efficiency. This paper examines AI applications, especially convolutional neural networks (CNNs), in diagnosing pulmonary diseases, such as pneumonia, tuberculosis, lung cancer. The goal to explore the impact of these technologies assess challenges their integration into clinical practice. Material methodsThis review based on articles from PubMed database, published between 2015 2024, using keywords artificial radiology, medicine, chest X-ray, chest-CT. ResultsAI, driven ML DL, has significantly enhanced medical imaging analysis, automating tasks that require expert interpretation. CNNs excel processing raw image data identifying hierarchical features, surpassing traditional methods diseases radiographs CT scans. systems demonstrate exceptional detecting cancer, providing rapid, consistent results, valuable resource-limited settings. However, persist, including need for diverse training datasets, model interpretability, existing workflows. ConclusionsAI, CNN-based DL models, reshaping advancing capabilities. While it often outperforms methods, best used complement human expertise. Overcoming quality, system integration, essential broader adoption. Continued research will enhance AI’s reliability utility, ultimately patient outcomes.

Language: Английский

Citations

0
Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection DOI Creative Commons
Opeyemi Adeniran, Blessing Ojeme,

Temitope Ezekiel Ajibola

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 163 - 163

Published: March 13, 2025

With the advancements in deep learning methods, AI systems now perform at same or higher level than human intelligence many complex real-world problems. The data and algorithmic opacity of models, however, make task comprehending input information, model, model’s decisions quite challenging. This lack transparency constitutes both a practical an ethical issue. For present study, it is major drawback to deployment methods mandated with detecting patterns prognosticating Alzheimer’s disease. Many approaches presented medical literature for overcoming this critical weakness are sometimes cost sacrificing accuracy interpretability. study attempt addressing challenge fostering reliability AI-driven healthcare solutions. explores few commonly used perturbation-based interpretability (LIME) gradient-based (Saliency Grad-CAM) visualizing explaining dataset, MRI image-based disease identification using diagnostic predictive strengths ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), Vision Transformer (ViT). experimental results show stacking achieving remarkable 98.0% while hard voting reached 97.0%. findings valuable contribution growing field explainable artificial (XAI) imaging, helping end users researchers gain understanding backstory behind image dataset decisions.

Language: Английский

Citations

0
Improving Biomedical Image Pattern Identification by Deep B4‐GraftingNet: Application to Pneumonia Detection DOI Creative Commons
Shishir Shah, Syed Taimoor Hussain Shah, Abdul Muiz Fayyaz

et al.

IET Image Processing, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT VGG‐16 and Inception are widely used CNN architectures for image classification, but they face challenges in target categorization. This study introduces B4‐GraftingNet, a novel deep learning model that integrates VGG‐16's hierarchical feature extraction with Inception's diversified receptive field strategy. The is trained on the OCT‐CXR dataset evaluated NIH‐CXR to ensure robust generalization. Unlike conventional approaches, B4‐GraftingNet incorporates binary particle swarm optimization (BPSO) selection grad‐CAM interpretability. Additionally, performed, multiple machine classifiers (SVM, KNN, random forest, naïve Bayes) determine optimal representation. achieves 94.01% accuracy, 94.22% sensitivity, 93.36% specificity, 95.18% F1‐score maintains 87.34% accuracy despite not being it. These results highlight model's superior classification performance, adaptability, potential real‐world deployment both medical general tasks.

Language: Английский

Citations

0