Eye Hypertension Disorder Through Ensemble Frame Networks: A Novel Approach for Early Identification and Segmentation DOI

T R Yashavanth,

Wahida Banu,

Rathan Kumar

et al.

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Nov. 20, 2024

Language: Английский

Histopathology-driven prostate cancer identification: A VBIR approach with CLAHE and GLCM insights DOI Creative Commons
Pramod Rangaiah, B. P. Pradeep Kumar, Robin Augustine

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 182, P. 109213 - 109213

Published: Oct. 2, 2024

Language: Английский

Citations

4
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
Enhanced perceptual wavelet packet features for spontaneous Kannada sentence recognition under uncontrolled conditions DOI
Mahadevaswamy Shanthamallappa, B. P. Pradeep Kumar

International Journal of Speech Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Language: Английский

Citations

0
Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications DOI Creative Commons
Pramod Rangaiah, B. P. Pradeep Kumar, Fredrik Huss

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 4, 2025

Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes methodology that combines use of advanced medical imaging with predictive modeling for improvement burn injury assessment. The proposed framework makes Adaptive Complex Independent Components Analysis (ACICA) Reference Region (TBSA) methods in conjunction deep learning techniques precise estimation depth Total Body Surface Area analysis. It also allows burns high accuracy, calculation TBSA, non-invasive analysis 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision detailed texture These technologies provide nuanced insights into severity, improving treatment planning. Our results demonstrate potential these to revolutionize care optimize patient outcomes.

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
Comparative Assessment of Machine Learning Models for Predicting Glucose Intolerance Risk DOI
B. P. Pradeep Kumar,

H. M. Manoj

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(7)

Published: Sept. 21, 2024

Language: Английский

Citations

3
Gauging Deep Learning Archetypal Effectiveness in Haematological Reclamation DOI
B. P. Pradeep Kumar,

J. Ravikumar

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(7)

Published: Oct. 15, 2024

Language: Английский

Citations

2
Eye Hypertension Disorder Through Ensemble Frame Networks: A Novel Approach for Early Identification and Segmentation DOI

T R Yashavanth,

Wahida Banu,

Rathan Kumar

et al.

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Nov. 20, 2024

Language: Английский

Citations

0