Comparative Analysis of Deep Neural Networks for Automated Ulcerative Colitis Severity Assessment DOI Creative Commons
Andreas Vezakis, Ioannis Vezakis,

Ουρανία Πετροπούλου

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(4), P. 413 - 413

Published: April 13, 2025

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by continuous inflammation of the colon and rectum. Accurate assessment essential for effective treatment, with endoscopic evaluation, particularly Mayo Endoscopic Score (MES), serving as key diagnostic tool. However, MES measurement can be subjective inconsistent, leading to variability in treatment decisions. Deep learning approaches have shown promise providing more objective standardized assessments UC severity. Methods: This study utilized publicly available images patients analyze compare performance state-of-the-art deep neural networks automated classification. Several architectures were tested determine most model grading The F1 score, accuracy, recall, precision calculated all models, statistical analysis was conducted verify statistically significant differences between networks. Results: VGG19 found best-performing network, achieving QWK score 0.876 macro-averaged 0.7528 across classes. among top-performing models very small suggesting that selection should depend on specific deployment requirements. Conclusions: demonstrates multiple network could automate severity Simpler achieve competitive results larger challenging assumption necessarily provide better clinical outcomes.

Language: Английский

Seeing Beyond: Advanced Image and Thermal Analysis for Early Detection of Diabetic Retinopathy and Diabetes DOI Open Access
Arvind Mewada, Sushil K. Maurya, M. A. Ansari

et al.

Biomedical & Pharmacology Journal, Journal Year: 2025, Volume and Issue: 18(December Spl Edition), P. 191 - 202

Published: Jan. 20, 2025

Diabetes mellitus (DM) is a chronic metabolic disorder condition that requires continuous monitoring and early detection to prevent serious complications such as diabetic retinopathy (DR) foot (DF) disease. In recent years, medical imaging technologies coupled with machine learning techniques have made progress in the automated of DM-related using retina or images. This article proposes novel Ens-DRDF model integrates ulcers advanced image processing techniques. The process involves removing optic disc blood vessels, followed by feature extraction, segmentation, classification. Fuzzy clustering aids lesion differentiation, enhancing quality for improved DR diagnosis.

Language: Английский

Citations

1
Comparative Analysis of Deep Neural Networks for Automated Ulcerative Colitis Severity Assessment DOI Creative Commons
Andreas Vezakis, Ioannis Vezakis,

Ουρανία Πετροπούλου

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(4), P. 413 - 413

Published: April 13, 2025

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by continuous inflammation of the colon and rectum. Accurate assessment essential for effective treatment, with endoscopic evaluation, particularly Mayo Endoscopic Score (MES), serving as key diagnostic tool. However, MES measurement can be subjective inconsistent, leading to variability in treatment decisions. Deep learning approaches have shown promise providing more objective standardized assessments UC severity. Methods: This study utilized publicly available images patients analyze compare performance state-of-the-art deep neural networks automated classification. Several architectures were tested determine most model grading The F1 score, accuracy, recall, precision calculated all models, statistical analysis was conducted verify statistically significant differences between networks. Results: VGG19 found best-performing network, achieving QWK score 0.876 macro-averaged 0.7528 across classes. among top-performing models very small suggesting that selection should depend on specific deployment requirements. Conclusions: demonstrates multiple network could automate severity Simpler achieve competitive results larger challenging assumption necessarily provide better clinical outcomes.

Language: Английский

Citations

0