arXiv subjects

Prabhakar Pradhan

Publications and source records attributed to Prabhakar Pradhan.

At least 19 recordsLinked to original sources

arXiv2026

Mesoscopic Light Localization and Inverse Participation Ratio Analysis of Tissue Structural Disorder for Optical Cancer Detection

We introduce a mesoscopic physics-based framework that transforms conventional transmission optical micrographs into quantitative maps of tissue structural heterogeneity using the Inverse Participation Ratio (IPR). For the first time, to our knowledge, IPR-based light-localization analysis is applied to cancer tissue imaging to quantify nano- to submicron-scale structural alterations through spatial fluctuations in tissue mass density or refractive index. Unlike conventional morphology-based assessment, this physics-driven approach provides objective structural biomarkers from label-free or routinely stained tissue images. The method establishes a scalable, reproducible platform for quantitative computational pathology and enhanced cancer diagnosis by integrating mesoscopic optical physics with standard optical microscopy.

physics.med-ph↗

arXiv2025

Alterations of brain tissue structural complexity and disorder in Alzheimer's disease (AD): Fractal, multifractal, fractal transformation, and disorder strength analyses

Alzheimer's disease (AD) is characterized by progressive microstructural deterioration in brain tissue, yet conventional imaging and histopathology often lack the sensitivity needed to detect subtle early-stage changes. Here, we present a multiparametric framework combining fractal and multifractal analysis and their distributions to quantify structural alterations in human brain tissue affected by AD. Moreover, from the fractal and multifractal formalism, we introduced an innovative fractal functional distribution method, a novel technique that transforms fractal distribution into a Gaussian form. Statistically, these distribution parameters are easy to interpret and can distinguish between control and diseased tissues. Across samples, we identify pronounced threshold-dependent behavior of fractal and multifractal parameters, reflecting the intrinsic sparsity and heterogeneous intensity landscape of brain tissue. These threshold-sensitive signatures provide a framework for quantitative stage detection and may serve as biomarkers for early pathological transitions. In addition, we studied structural disorder and complexity using our established light localization technique, inverse participation ratio (IPR) analysis. IPR-based analysis demonstrates that increasing IPR pixel size highlights the elevation of structural alterations with disease progression. Together, these integrative analyses establish a robust, multi-scale quantitative framework for detecting microstructural alterations in AD, providing a promising foundation for early diagnosis and improved pathological assessment.

physics.med-ph↗

arXiv2025

Quantitative Characterization of Brain Tissue Alterations in Brain Cancer Using Fractal, Multifractal, and IPR Metrics

We studied the structural alterations between healthy and diseased brain tissues using a multiparametric framework combining fractal analysis, fractal functional transformation, multifractal analysis, and the Inverse Participation Ratio (IPR) analysis. Accurate characterization of brain tissue microstructure is crucial for early detection and diagnosis of cancer. By applying box-counting methods on brightfield microscopy images, we estimated the fractal dimension (Df) and its logarithmic (ln(Df)) and functional (ln(Dtf)) forms to highlight spatial irregularities in the tissue architecture. While Df and ln(Df) exhibited long-tailed distributions distinguishing healthy from cancer tissues, ln(Dtf) provided significantly improved differentiation by emphasizing local structural variations. Additionally, multifractal analysis revealed broader f(α) vs α curves in cancerous samples, reflecting higher heterogeneity. IPR analysis based on light localization further demonstrated increased nanoscale variations in mass density, reflecting higher structural disorder in cancer tissues. Combining these complementary approaches creates a robust framework for measuring tissue complexity and holds great potential to improve microscopic diagnostic methods for brain cancer detection.

physics.med-ph↗

arXiv2025

Alterations of brain tissue structural complexity and disorder in Parkinson disease (PD): Fractal, multifractal, fractal transformation, and disorder strength analyses

Parkinson disease (PD) is marked by progressive neurodegeneration, yet early and subtle structural alterations in brain tissue remain difficult to detect with conventional imaging and analytical methods. Fractal and multifractal frameworks offer a principled way to quantify complex biological architecture, but their diagnostic utility in PD has been largely unexplored. In this study, we investigated the fractal and multifractal characteristics of human brain tissues to identify structural alterations associated with PD. Alongside conventional fractal and multifractal analysis, we employed a recently developed fractal functional distribution method that transforms distributions into a Gaussian form, thereby enhancing quantification. Using this combined approach, we found notable deviations across multiple distribution metrics in PD samples, offering potential for quantitative staging and diagnostic applications. The multifractal analysis revealed threshold-dependent variations in intensity-based measures, which are linked to the sparsity and heterogeneity of neural tissue and suggestive of potential biomarker value. Additionally, we applied inverse participation ratio (IPR) analysis to assess structural disorder, demonstrating that larger IPR pixel sizes correlate with increased structural complexity during disease progression. These complementary analyses outline a multi-layered quantitative profile of PD-related tissue disruption, offering a foundation for earlier, objective assessment of disease-associated microstructural change.

physics.med-ph↗

arXiv2025

Comprehensive Multimodal and Multiscale Analysis of Alzheimer Disease in 5xFAD Mice: Optical Spectroscopies, TEM, Neuropathological, and Behavioral Investigations

Alzheimer disease (AD) is considered one of the leading causes of death in the United States, and there is no effective cure for it. Understanding the neuropathological mechanisms underlying AD is essential for identifying early, reliable biomarkers and developing effective therapies. In this paper, we report on a comprehensive multimodal study of AD pathology using the 5xFAD mouse model. We employed light-scattering techniques, Partial Wave Spectroscopy (PWS) and Inverse Participation Ratio (IPR), to detect nanoscale structural alterations in brain tissues, nuclear components, and mitochondria. To support the light-scattering experiments, behavior, and histopathological studies were conducted. These analyses revealed significant increases in structural heterogeneity and mass density fluctuations in the brains of 5xFAD mice compared with Non-transgenic controls. Behavioral assessment demonstrated memory impairment in 5xFAD mice, reflected by a reduced recognition index. Histopathological analysis further revealed increased amyloid beta plaques and microglia activation in the hippocampus and cortex of 5xFAD mice compared with Non-transgenic controls. An increase in structural disorder within brain tissues can be attributed to higher mass density fluctuations, likely arising from macromolecular rearrangement driven by amyloid beta aggregation and neuroinflammatory responses as the disease progresses. Our findings suggest that PWS and IPR-derived metrics provide sensitive biophysical indicators of early cellular and subcellular disruption, offering potential as quantitative biomarkers for the detection and progression of AD.

physics.med-ph↗

arXiv2025

Decoding Breast Cancer in X-ray Mammograms: A Multi-Parameter Approach Using Fractals, Multifractals, and Structural Disorder Analysis

We explored the fractal and multifractal characteristics of breast mammogram micrographs to identify quantitative biomarkers associated with breast cancer progression. In addition to conventional fractal and multifractal analyses, we employed a recently developed fractal-functional distribution method, which transforms fractal measures into Gaussian distributions for more robust statistical interpretation. Given the sparsity of mammogram intensity data, we also analyzed how variations in intensity thresholds, used for binary transformations of the fractal dimension, follow unique trajectories that may serve as novel indicators of disease progression. Our findings demonstrate that fractal, multifractal, and fractal-functional parameters effectively differentiate between benign and cancerous tissue. Furthermore, the threshold-dependent behavior of intensity-based fractal measures presents distinct patterns in cancer cases. To complement these analyses, we applied the Inverse Participation Ratio (IPR) light localization technique to quantify structural disorder at the microscopic level. This multi-parametric approach, integrating spatial complexity and structural disorder metrics, offers a promising framework for enhancing the sensitivity and specificity of breast cancer detection.

physics.med-ph↗

arXiv2025

Evaluation and optimization of deep learning models for enhanced detection of brain cancer using transmission optical microscopy of thin brain tissue samples

Optical transmission spectroscopy is one method to understand brain tissue structural properties from brain tissue biopsy samples, yet manual interpretation is resource intensive and prone to inter observer variability. Deep convolutional neural networks (CNNs) offer automated feature learning directly from raw brightfield images. Here, we evaluate ResNet50 and DenseNet121 on a curated dataset of 2,931 bright-field transmission optical microscopy images of thin brain tissue, split into 1,996 for training, 437 for validation, and 498 for testing. Our two stage transfer learning protocol involves initial training of a classifier head on frozen pretrained feature extractors, followed by fine tuning of deeper convolutional blocks with extensive data augmentation (rotations, flips, intensity jitter) and early stopping. DenseNet121 achieves 88.35 percent test accuracy, 0.9614 precision, 0.8667 recall, and 0.9116 F1 score the best performance compared to ResNet50 (82.12 percent, 0.9035, 0.8142, 0.8563). Detailed analysis of confusion matrices, training and validation curves, and classwise prediction distributions illustrates robust convergence and minimal bias. These findings demonstrate the superior generalization of dense connectivity on limited medical datasets and outline future directions for multi-class tumor grading and clinical translation.

physics.med-ph↗

arXiv2025

Automated grading and staging of ovarian cancer using deep learning on the transmission optical microscopy bright-field images of thin biopsy tissue samples

Ovarian cancer remains a challenging malignancy to diagnose and manage, with prognosis heavily dependent on the stage at detection. Accurate grading and staging, primarily based on histopathological examination of biopsy tissue samples, are crucial for treatment planning and predicting outcomes. However, this manual process is time-consuming and subject to inter-observer variability among pathologists. The increasing volume of digital histopathology slides necessitates the development of robust, automated methods to assist in this critical diagnostic step for ovarian cancer. (Methods) This study presents a deep learning framework for the automated prediction of ovarian cancer stage (classified into five categories: 0, I, II, III, IV) using routine histopathological images. We employed a transfer learning approach, fine-tuning a ResNet-101 convolutional neural network pre-trained on ImageNet. The training process incorporated comprehensive data augmentation, weighted random sampling, and class weighting to address dataset characteristics. Hyperparameter optimization for learning rate, dropout rate, and weight decay was performed using a genetic algorithm to enhance model performance and generalization. (Results) Evaluated on an independent test set of ovarian thin tissue brightfield images, the developed model achieved a high overall classification accuracy of 97.62%.

physics.med-ph↗

arXiv2025

Finer-Focused Partial Wave Spectroscopy (ff-PWS) and Detection of Cancer Stages From Human Tissue Samples

The progression of cancer is associated with different genetic and epigenetic events which result in nano to microscale structural alterations in cells/tissue. However, these structural alterations in the early stage of the disease remain undetectable by conventional microscopy due to the diffraction-limited resolution of ~200nm. With cancer being an epidemic worldwide, early and accurate detection methods are always in demand. In this sense, we developed a finer focusing mesoscopic physics-based partial wave spectroscopy (PWS) imaging and quantification technique, which can probe the precise scattering volume in cells/tissue to detect such structural alterations. Therefore, we employ the highly sensitive PWS technique to quantify the nanoscale refractive index fluctuations using commercially available paraffin embedded tissue microarrays (TMA) samples with the goal of the standardized early and accurate detection of stages of different cancers. Recently the use of scientific TMA samples has gained research interest in diagnosing different diseases and drug-effect in treatment since they are commercially easily accessible. In this work, deadly cancer such as pancreatic, prostate, breast, and colon cancer TMAs containing multiple cores of different stages for each cancer are analyzed using the PWS technique and the degree of disorder strength Ld was quantified. The finer focusing PWS results show that an increase in the disorder strength with the increase in the stage of each cancer. This quantitative analysis of different cancer stages using commercially available TMA samples could enhance and standardize early, accurate cancer diagnosis modalities.

physics.med-ph↗

arXiv2024

Photonics detection of molecular-specific spatial structural alterations in cell nuclei due to chronic alcoholism and probiotics treatments on colon cancer via a light localization method using confocal imaging

Photonics/light localization techniques are important in understanding the structural changes in biological tissues at the nano- to sub-micron scale. It is now known that structural alteration starts at the nanoscale at the beginning of cancer progression. This study examines the molecular-specific nano-structural alterations of chronic alcoholism and probiotic effects on colon cancer using a mouse model of colon cancer. Confocal microscopy and mesoscopic light-scattering analysis are applied to quantify structural changes in DNA (chromatin), cytoskeleton, and ki-67 protein cells with appropriate staining dyes. We assessed alcohol-treated and azoxymethane (AOM) with dextran sulfate sodium (DSS)-induced colitis models, including ethanol (EtOH) and probiotic (L.Casei) treatments separately and together. The inverse participation ratio (IPR) technique was employed to quantify the degree of light localization to access the molecular-specific spatial structural disorder as a biomarker for cancer progression detection. Significant enhancement of cancer progression was observed in the alcohol-treated group, and probiotics treatment with alcohol showed partial reversal of these changes in colon cancer. The results underscore the potential of the IPR technique in detecting early structural changes in colon cancer, offering insights into the mitigating effects of probiotics on alcohol-induced enhancement of colon cancer.

physics.med-ph↗

arXiv2024

Optical probing of fractal and multifractal connection to structural disorder in weakly optical disordered media: Application to cancer detection

The light scattering experiment establishes a relationship between refractive index fluctuations and fractal dimension in weakly scattering tissue-like media. Based on the box-counting approach, an analytical model is developed and shows that the fractal dimension has a functional dependency on the structural disorder or refractive index fluctuation for short-range correlation and approximately linearly depends on each other for tissue-like media. Several parametric imaging systems can be connected using this approach. Further, tissue's weak multifractality optical scattering is explored using the box-counting method. It is shown that with a functional transformation, the distribution follows lognormal distributions.

physics.optics↗

arXiv2024

Optical detection of the spatial structural alteration in the human brain tissues and cells and DNA and chromatin due to Parkinsons disease

Parkinsons disease (PD) is considered one of the most frequent neurological diseases in the world. There is a need to study the early and efficient biomarkers of Parkinsons, such as changes in structural disorders like DNA and chromatin, especially at the subcellular level in the human brain. We used two techniques, Partial wave spectroscopy (PWS) and Inverse Participation Ratio (IPR), to detect the changes in structural disorder in the human brain tissue samples. It was observed from the PWS experiment that there was an increase in structural disorder in Parkinsons disease tissues and cells when compared to normal tissues and cells using mesoscopic light transport theory. Furthermore, the IPR experiment also showed DNA and chromatin structural alterations that have the same trend and support the PWS results. The increase in mass density in the nuclei components, such as DNA and chromatin, can be linked to the aggregation of alpha-synuclein in the substantia nigra of the brain. This protein deposition is considered a significant cause of neuronal death in the brains of PD patients. We also did a histological analysis of brain tissues, which supports our results from dual photonics techniques. The results show that this dual technique is a powerful approach to detect the changes. Our results highlight the potential of the parameter, related to the structural disorder strength, as an efficient biomarker for PD progress, paving the way for research into early disease detection.

physics.med-ph↗

arXiv2024

An AI-directed analytical study on the optical transmission microscopic images of Pseudomonas aeruginosa in planktonic and biofilm states

Biofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed.

physics.med-ph↗

arXiv2024

Dual Photonics Probing of Nano- to Submicron-Scale Structural Alterations in Human Brain Tissues or Cells and Chromatin or DNA with the Progression of Alzheimers Disease

Understanding alterations in structural disorders in tissue or cells or building blocks, such as DNA or chromatin in the human brain, at the nano to submicron level provides us with efficient biomarkers for Alzheimers detection. Here, we report a dual photonics technique to detect nano- to submicron-scale alterations in brain tissues or cells and DNA or chromatin due to the early to late progression of Alzheimers disease in humans. Using a recently developed mesoscopic light transport technique, fine-focused nano-sensitive partial wave spectroscopy (PWS), we measure the degree of structural disorder in tissues. Furthermore, the chemical-specific inverse participation ratio technique (IPR) was used to measure the DNA or chromatin structural alterations. The results of the PWS and IPR experiments showed a significant increase in the degree of structural disorder at the nano to submicron scale at different stages of AD relative to their controls for both the tissue or cell and DNA cellular levels. The increase in the structural disorder in cells or tissues and DNA or chromatin in the nuclei can be attributed to higher mass density fluctuations in the tissue and DNA or chromatin damage in the nuclei caused by the rearrangements of macromolecules due to the deposition of the amyloid beta protein and damage in DNA or chromatin with the progress of AD.

physics.med-ph↗

arXiv2020

Detection of cancer stages through fractal dimension analysis of tissue microarrays (TMA) via optical transmission microscopy

Tissues are fractal due to its self-similar structure, and the fractal dimension change with the abnormalities such as in disease like cancer. The optical imaging of thin slices of tissue using transmission microscopy can produce an intensity distribution pattern proportional to its refractive index which represents the mass density distribution pattern of the tissues. The fractal dimension of tissue is calculated by analyzing this mass density distribution and is known to increase with the progression of carcinogenesis. This paper explores the viability of using this quantitative approach of fractal dimension analysis to create a standardized accurate cancer diagnosis test and staging which reduces the issues plaguing current testing methods. A commonality for most of the deadliest cancers is their lethality due to the difficulty in properly diagnosing them in the early stages or accurate late staging. This difficulty can arise from the physical location of the organ causing them to be hard to access and the lack of noticeable symptoms until the late stages when there is almost no hope of treatment. To study some of these deadly cancers, commercially available paraffin embedded tissue microarray (TMA) samples containing multiple cores of different cases and stages of Pancreatic, Breast, Colon, and Prostate cancer are analyzed. The fractal dimension of different TMA samples is able to correctly differentiate between the different stages of each cancer, raising the possibility of a standardized system being created to increase diagnosis accuracy in the future.

physics.bio-ph↗

arXiv2020

Photonics probing of DNA specific spatial mass density fluctuations in gut cell nuclei due to total body irradiation via confocal imaging

Abnormalities within cells result in nanoscale structural alterations can be characterized via confocal imaging and quantification of these alterations. Accidental or deliberate exposure to total body irradiation (TBI) have adverse effects on the nuclear DNAs of cells. Here, we study the DNA molecular mass density spatial structural alterations of chromatin in cell nuclei of gut tissues caused by the exposure to standard doses of 4Gy TBI, using the light localization technique called inverse participation ratio (IPR) in confocal images. Results indicate radiation suppresses DNA spatial mass density fluctuations. And hence, reduction and saturation in DNA density fluctuations are observed for different durations of post-irradiation.

physics.med-ph↗

arXiv2019

Photonics study of probiotic treatment on brain cells exposed to chronic alcoholism using molecular specific nuclear light localization properties via confocal imaging

Molecular specific photonics localization technique, the inverse participation ratio (IPR), is a powerful technique to probe the nanoscale structural alterations due to abnormalities or chronic alcoholism in brain cells using the confocal image. Chronic alcoholism is correlated with medical, behavioral, and psychological problems including brain cell damage. However, probiotics such as Lactobacillus Plantarum has shown the promising result in soothing the human brain. This report, using the Confocal-IPR technique, nano to submicron scale structural abnormalities of the glial cells and the nuclei of alcoholic mice brain in the presence of probiotics. The increase in the structural disorder of alcoholic brain cells while the decrease or normalcy in the structural disorder of brain cells of mice fed with probiotics and alcohol simultaneously indicates that alcohol stimulates probiotics and enhances brain function.

physics.bio-ph↗

arXiv2019

Optical probing of pups brain tissue and molecular specific nuclear nano-structural alterations due to fetal alcoholism via dual spectroscopic approach

Mesoscopic physics-based dual spectroscopic imaging techniques, partial wave spectroscopy (PWS) and inverse participation ratio (IPR), are used to quantify the nano to submicron scales structural alterations in postnatal pups brain cells and tissues due to fetal alcoholism. Chronic alcoholism during pregnancy, being teratogenic, results in fetal alcohol syndrome and neurological disorder. Results of PWS studies of brain tissues show a higher degree of structural alterations. Furthermore, the IPR analyses of cell nuclei show that spatial molecular mass density structural disorder increases in DNA while decreases for histone. This study characterize the brain spatial structures from molecular to tissue level in fetal alcoholism.

physics.med-ph↗