The Burn Grafting Image Reclamation Redefined with the Peak-Valley Approach DOI
B. P. Pradeep Kumar,

E. Naresh,

A. Ashwitha

et al.

Critical Reviews in Biomedical Engineering, Journal Year: 2025, Volume and Issue: 53(2), P. 21 - 35

Published: Jan. 1, 2025

Burn injuries constitute a significant public health challenge, often necessitating the expertise of medical professionals for diagnosis. However, in scenarios where specialized facilities are unavailable, utility automated burn assessment tools becomes evident. Factors such as area, depth, and location play pivotal role determining severity. In this study, we present classification model diagnosis, leveraging machine learning techniques. Our approach includes an image reclamation system that incorporates peak valley algorithm, ensuring removal noise while consistently delivering high-quality results. By using skewness kurtosis, demonstrate substantial improvements diagnostic accuracy. proposed sources key features from enhanced grafting samples transformation, enabling computation BQs unique bin analysis to enhance reclamation. experimental results highlight efficiency gains, notably growing matching graft 14 images. The intended work involves creation model. utilizes support vector (SVM). evaluation will be conducted untrained catalogue, with specific focus on its effectiveness reclaiming images necessitate grafts distinguishing them those do not. holds promise sample emergency settings, thereby expediting more accurate diagnoses treatments acute injuries. This has latent save lives improve patient upshots traumas.

Language: Английский

Enhanced Glaucoma Detection Using U-Net and U-Net+ Architectures Using Deep Learning Techniques DOI
Pradeep Kumar, Pramod Rangaiah, Robin Augustine

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
The Burn Grafting Image Reclamation Redefined with the Peak-Valley Approach DOI
B. P. Pradeep Kumar,

E. Naresh,

A. Ashwitha

et al.

Critical Reviews in Biomedical Engineering, Journal Year: 2025, Volume and Issue: 53(2), P. 21 - 35

Published: Jan. 1, 2025

Burn injuries constitute a significant public health challenge, often necessitating the expertise of medical professionals for diagnosis. However, in scenarios where specialized facilities are unavailable, utility automated burn assessment tools becomes evident. Factors such as area, depth, and location play pivotal role determining severity. In this study, we present classification model diagnosis, leveraging machine learning techniques. Our approach includes an image reclamation system that incorporates peak valley algorithm, ensuring removal noise while consistently delivering high-quality results. By using skewness kurtosis, demonstrate substantial improvements diagnostic accuracy. proposed sources key features from enhanced grafting samples transformation, enabling computation BQs unique bin analysis to enhance reclamation. experimental results highlight efficiency gains, notably growing matching graft 14 images. The intended work involves creation model. utilizes support vector (SVM). evaluation will be conducted untrained catalogue, with specific focus on its effectiveness reclaiming images necessitate grafts distinguishing them those do not. holds promise sample emergency settings, thereby expediting more accurate diagnoses treatments acute injuries. This has latent save lives improve patient upshots traumas.

Language: Английский

Citations

0