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

Background: The human biofield serves as an indicator of an individual's physical and emotional health status. Biofield-based therapeutic techniques, also known as complementary and alternative medicine (CAM) techniques such as Reiki, Therapeutic Touch, and Pranic Healing, leverage this information in the preliminary assessment phase before treatment initiation. These modalities are increasingly integrated as complementary methods within health diagnostic frameworks. Among the techniques employed for biofield visualization, gas discharge visualization (GDV) and polycontrast interference photography (PIP) are the predominant imaging methodologies. Notably, the majority of scientific investigations and empirical studies have primarily utilized GDV-derived images, with comparatively fewer studies focusing on PIP-based data. Purpose: The primary objective of this study is to identify energy imbalances within the pancreatic region using biofield imaging and to utilize these patterns for classifying subjects as diabetic or nondiabetic. This work emphasizes the relevance of biofield information in health assessment and evaluates its potential for supporting energy-based diagnostic approaches. Materials and Methods: Color-based clustering methods were applied for segmentation. A transfer learning-based ensemble framework was developed using pretrained convolutional neural network (CNN) architectures ConvNeXtBase and ResNet50 to classify biofield images into diabetic and nondiabetic categories. Grid search optimization identified the optimal hyperparameters, which were applied during fine-tuning to improve feature learning. Ensemble model was evaluated, with the ConvNeXtBase + ResNet50 combination achieving the highest accuracy of 99.12%. Robust performance validation was ensured using 5-fold cross-validation to minimize sampling bias and enhance generalization. Results: The ensemble of ResNet50 and ConvNeXtBase achieved the highest accuracy of 99.12%, outperforming individual models (ConvNeXtBase: 97.93% and ResNet50: 96.28%). Receiver operating characteristic analysis confirmed strong reliability with area under the curve values above 0.99 for both classes (diabetic and nondiabetic). The 5-fold cross-validation analysis further demonstrated the robustness of the proposed ensemble model, achieving a mean accuracy of 97.45%, indicating highly consistent performance across different dataset partitions. Conclusions: The CNN-based models can be trained to classify the biofield images, and this approach can enable automated analysis of biofield images. The approach of using clustering, deep learning, and ensemble modeling as analyzed and described in this study seems to be highly effective. The overall system of biofield imaging and automated clustering can act as a potential noninvasive diagnostic support tool, though further testing with larger datasets and expert validation is necessary for clinical application.