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Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Transfer Learning-driven Biofield Image Analysis for Predictive Modeling of Diabetes.

Shivanand S Gornale¹, Supriya Shankar Patil¹, Anup Waman Deo²

¹Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India.

Journal of Medical Physics

|April 27, 2026

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This study developed a deep learning model to classify biofield images, achieving 99.12% accuracy in distinguishing diabetic from nondiabetic individuals. This noninvasive approach shows promise for automated health diagnostics.

Area of Science:

Biofield imaging analysis
Computational diagnostics
Medical imaging and machine learning

Background:

The human biofield reflects physical and emotional health.
Biofield therapies (e.g., Reiki) use this information for assessment.
Gas Discharge Visualization (GDV) and Polycontrast Interference Photography (PIP) are key biofield imaging techniques.

Purpose of the Study:

To detect pancreatic energy imbalances via biofield imaging.
To classify subjects as diabetic or nondiabetic based on biofield patterns.
To evaluate biofield information's potential in energy-based diagnostics.

Main Methods:

Applied color-based clustering for image segmentation.
Developed an ensemble deep learning framework using ConvNeXtBase and ResNet50.

Keywords:

Biofield image clustering image segmentation deep learning techniques gas discharge visualization hyperparameter tuning pancreas energy polycontrast interference photography transfer learning techniques

Related Experiment Videos

Last Updated: Apr 28, 2026

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Utilized grid search for hyperparameter optimization and 5-fold cross-validation.

Main Results:

The ConvNeXtBase + ResNet50 ensemble achieved 99.12% accuracy.
Individual models showed high accuracy (ConvNeXtBase: 97.93%, ResNet50: 96.28%).
Area Under the Curve (AUC) values exceeded 0.99, confirming strong reliability.

Conclusions:

Convolutional Neural Network (CNN) models can automate biofield image analysis.
Clustering, deep learning, and ensemble modeling offer an effective diagnostic approach.
This system could serve as a noninvasive diagnostic support tool, pending further validation.