Applications In Medical Diagnosis Research Articles

Moiré metasurfaces with tunable near-infrared-I chiroptical responses for biomolecular chirality discrimination.

Manipulating circular dichroism in chiral metasurfaces has been increasingly important for a wide range of polarization-sensitive photonic applications. However, simple methods for presenting chiral nanostructures with tunable and considerable chiroptical responses in the near-infrared-I regime remains underexplored. Herein, two sheets of suspended symmetric bilayer metagratings fabricated via single-step electron beam lithography are stacked into a moiré metasurface with its circular dichroism value reaching up to 20.9°. The chirality of the moiré metasurface can be fully tuned in terms of both its sign and magnitude by adjusting the in-plane angle between the two twisted sheets of metagratings. The multilayered design is accessible to the coupling of hybridized plasmons for governing the chiroptical properties in the near-infrared-I regime. The ratio between the resonance wavelength and the grating period is about 1.65, which is much lower compared to that in most existing moiré metasurfaces with strong chiroptical responses. Furthermore, the superchiral fields in the inter-sheet region are further exploited for label-free enantiodiscrimination with ultrahigh sensitivity of 10.17 nm fmol-1 mm2. The proposed moiré metasurfaces with strong near-infrared-I chirality hold great potential for supporting polarization engineering and biomolecular detection, paving a way for advanced applications in medical diagnosis, biomedical imaging, and display technologies.

Correction: Novel distance measures on complex picture fuzzy environment: applications in pattern recognition, medical diagnosis and clustering

Correction: Novel distance measures on complex picture fuzzy environment: applications in pattern recognition, medical diagnosis and clustering

Ca2+/Si4+ Modification of the (Gd,Lu)AG Garnet for Enhanced Broadband Cr3+ Luminescence of High Thermal Stability.

Near-infrared (NIR)-emitting phosphors with high quantum efficiency and thermal stability are crucial to NIR pc-LEDs. Garnet-structured (GdLuCa)(Al4-zSiCrz)O12 (z = 0.01-0.2) and (Gd2-xLuCax)(Al4.95-xSixCr0.05)O12 (x = 0.2-1.0) new phosphors with promising NIR luminescence under blue light excitation were designed and fabricated by a solid-state reaction in this work. It was analyzed that the Ca2+, Cr3+, and Si4+ ions would replace Gd3+ in [GdO8], Al1 in [Al1O6], and Al2 in [Al2O4], respectively, and the optimal Cr3+ content is z = 0.05, above which concentration quenching would occur via an electric dipole-dipole interaction. Increasing Ca2+/Si4+ substitution up to x = 1.0 led to luminescence enhancement by a factor of up to 1.85 and internal/external quantum efficiency (%) increment from ∼25.9/10.7 to 63.4/27.5, and all of the phosphors showed excellent thermal stability (I423K/I298K ≥ 87.6%). The luminescence properties of Cr3+ were discussed in detail through systematic investigation of the effects of Cr3+ and Ca2+/Si4+ contents on the crystal structure, local coordination, and crystal field. With the NIR pc-LED device integrated from the optimal phosphor (x = 1.0) and a blue LED chip, electroluminescence manifested potential applications in night vision and medical diagnosis.

A hybrid diversited attribute group decision-making method based on non-additive measure entropy weight and multi-granularity generalized fuzzy rough set with application in medical diagnosis

A hybrid diversited attribute group decision-making method based on non-additive measure entropy weight and multi-granularity generalized fuzzy rough set with application in medical diagnosis

Insights of phosphor charging upon exposure to intense illumination

Storage phosphors, known for their delayed emission after charging, have shown great potential for applications in radiation detection, bio-imaging, and medical diagnosis. Despite their promise, the underlying mechanisms governing their charging behavior have not been sufficiently investigated. In this Letter, we elucidate the response of storage phosphors, focusing on the Y3Sc2Ga3O12:Pr3+ composition, when subjected to intense illumination. Our findings reveal that upon exposure to 266 nm ultraviolet or 450 nm blue lasers, the phosphor efficiently captures excitation energy through direct photoionization or upconversion charging (UCC), leading to subsequent trap filling. Thermoluminescence measurements, incorporating variations in irradiation power and dose, confirm that UCC enables an impressive fast charging time of 0.01 s. The unique two-step ionization and nonlinear charging characteristics of UCC are visually demonstrated using a portable laser engraver. Furthermore, we illustrate the versatility of UCC across different phosphor systems, highlighting its potential for advanced information storage applications. This research significantly advances the understanding of phosphor charging dynamics, paving the way for future innovations in luminescent materials.

Highly stretchable, self-healing, adhesive, 3D-printable and antibacterial double-network hydrogels for multifunctional wearable sensors.

Conductive hydrogels based on sodium alginate (SA) have potential applications in human activity monitoring and personal medical diagnosis due to their good conductivity and flexibility. However, most sensing SA-hydrogels exhibit poor mechanical properties and lack of self-healing, self-adhesive, and antibacterial properties, greatly limiting their practical applications. Therefore, in this paper, a multifunctional double-network PAA-SA hydrogel consisting of poly(acrylic acid) (PAA) and sodium alginate (SA) was prepared by a simple strategy. As a rigid network structure, SA endowed the hydrogel double network structure with excellent mechanical performance. As a wearable sensor, the PAA-SA hydrogel exhibited excellent tensile properties (strain: 1799.2 %), self-healing, high sensitivity (GF = 9.9), reliable repeatability, self-adhesive, 3D printability and antibacterial activity. Additionally, the highly sensitive wearing sensing PAA-SA hydrogel could accurately and real-time monitor various intense or subtle human movements, such as joint bending, face and throat vibration. Moreover, PAA-SA hydrogels were not only used for handwritten recognition of Arabic numerals and English letters, but also for real-time sensing of temperature changes and monitoring of human sweating. The prepared multifunctional wearable sensing hydrogel has the advantages of simple and versatile methods and low cost, making it a promising candidate for applications in different fields such as electronic skin, soft robotics, and medical monitoring.

Novel distance measures on complex picture fuzzy environment: applications in pattern recognition, medical diagnosis and clustering

Novel distance measures on complex picture fuzzy environment: applications in pattern recognition, medical diagnosis and clustering

Some Similarity Measures on Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets and Their Applications in Medical Diagnosis

In recent years, the application of fuzzy sets has gained significant attraction in various fields, including medical diagnosis, due to their ability to manage uncertainties and imprecise information. This paper focuses on the comparative analysis of similarity measures within the realm of Generalized Interval-Valued Intuitionistic Fuzzy Soft Expert Sets (GIVIFSESs) and explores their application in the domain of medical diagnosis. Most of the important topics in fuzzy set theory are the similarity measures between the generalizations of fuzzy set theory. Similarity measures are a crucial tool which was used in data science. In this process, we measure how much the data sets are related and comparable. Measures of similarity give a numerical value that reveals the strength of associations between sets or sets of variables. In this paper, we initiate a new concept of generalized interval-valued intuitionistic fuzzy soft expert sets and their fundamental operations. This new concept is more flexible than existing concepts based on their algebraic definition. Unlike fuzzy sets, the concept of generalized interval-valued intuitionistic fuzzy soft expert sets is characterized by a degree of membership and degree of non-membership along with fuzzy set theory. The proposed methodology is validated through an empirical application in medical diagnosis, where (GIV-IFSESs) are employed to model the uncertainty and imprecision inherent in expert assessments. The selected similarity measures are then applied to quantify the degree of resemblance between different medical cases, facilitating a more informed decision-making process. We introduce several types of similarity measures on generalized interval-valued intuitionistic fuzzy soft expert sets. We also discuss a similarity measure of Type-I, Type-II, and Type-III for two (GIVIFSESs) and its application in medical diagnosis problems.

Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost

This study aims to develop a heart disease classification model using an ensemble approach by leveraging a Stacking framework that combines BiGRU, BiLSTM, and XGBoost models. In this research, the BiGRU and BiLSTM models are utilized as base models to extract temporal and spatial features from sequential data, while XGBoost is employed as a metamodel to perform the final classification based on the features generated by the two base models. The test results show that the BiGRU model achieves an accuracy of 0.77, while the BiLSTM model achieves an accuracy of 0.85. By applying the Stacking technique using XGBoost as the meta-model, the classification accuracy significantly increases to 0.92. These findings indicate that the Stacking framework can effectively enhance heart disease classification performance, making it a potentially powerful tool for medical applications in heart disease diagnosis.

Applications of distance measure between dual hesitant fuzzy sets in medical diagnosis and weighted dual hesitant fuzzy sets in making decision

This paper introduces a novel distance measure for dual hesitant fuzzy sets (DHFS) and weighted dual hesitant fuzzy sets (WDHFS), with a rigorous proof of the triangular inequality to ensure its mathematical validity. The proposed measure extends the normalized Hamming, generalized, and Euclidean distance measures to dual hesitant fuzzy elements (DHFE), offering a broader framework for handling uncertainty in fuzzy environments. Additionally, the utilization of a score function is shown to simplify the computation of these distance measures. The practical relevance of the proposed measure is demonstrated through its application in medical diagnosis and decision-making processes. A comparative analysis between the newly introduced distance measure denoted as χ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi$$\\end{document}, and an existing measure, χ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\chi _1$$\\end{document} is performed to underscore the superiority and potential advantages of the new approach in real-world scenarios.

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health.

Circular Linear Diophantine Fuzzy Sets and Their Application in Medical Diagnosis

To address the uncertainty and imprecision in a complex system, the concept of the linear Diophantine fuzzy set (LDFS) was introduced, which is a fuzzy set extension that eliminates the constraints of current methodologies and gives the decision-maker complete freedom to select the grades, producing results that are expressive and flexible. A circular intuitionistic fuzzy set (C-IFS) is depicted by a circle with the membership and non-membership values as its center coordinates, addressing the ambiguity regarding membership and non-membership values. In this paper, we propose the notion of a circular linear Diophantine fuzzy set (C-LDFS) as a hybrid structure of both circular intuitionistic fuzzy sets and linear Diophantine fuzzy sets. In addition, some fundamental set operations on C-LDFSs are presented. Furthermore, a similarity metric for C-LDFS is also introduced in the paper. Additionally, we confirm the axiomatic requirement of the similarity measure and highlight a few of its traits. Finally, the proposed similarity metric is implemented in clinical decision-making to illustrate its efficacy.

Enhanced WGAN Model for Diagnosing Laryngeal Carcinoma

This study modifies the U-Net architecture for pixel-based segmentation to automatically classify lesions in laryngeal endoscopic images. The advanced U-Net incorporates five-level encoders and decoders, with an autoencoder layer to derive latent vectors representing the image characteristics. To enhance performance, a WGAN was implemented to address common issues such as mode collapse and gradient explosion found in traditional GANs. The dataset consisted of 8171 images labeled with polygons in seven colors. Evaluation metrics, including the F1 score and intersection over union, revealed that benign tumors were detected with lower accuracy compared to other lesions, while cancers achieved notably high accuracy. The model demonstrated an overall accuracy rate of 99%. This enhanced U-Net model shows strong potential in improving cancer detection, reducing diagnostic errors, and enhancing early diagnosis in medical applications.

A Cubic Pure Half-Spinor Approach to High Performance Photon-Counting Computerized X-ray Tomography – Transcendence of the Brauer Mirror Symmetry Functor –

The goal of this paper is to present a spectral theory of advanced photon-counting computerized X-ray tomography, which is a promising microelectronical semiconductor wafer X-ray technique on the verge of becoming clinically feasible and has the potential to dramatically alter the clinical application of computer-aided medical diagnosis in the upcoming decades. In analogy to the deep fact that any real division algebra has dimension 1, 2, 4 or 8, the isogeneous third Galois cohomological, mirror symmetrical approach to high spatial resolution spectral photon-counting computerized tomography establishes that the exceptional Lie group Spin(8,R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Spin}(8,\\mathbb {R})$$\\end{document} is the only one of the spin group spectrum {Spin(n,R)∣n≥1}\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\{\ extrm{Spin}(n,\\mathbb {R})\\mid n \\ge 1\\}$$\\end{document} to admit a triality automorphism. It is the exceptional phenomenon of triality which is able to produce by the spin representation theory of the exceptional Lie group of octonions O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {O}$$\\end{document} the spectral shaped digital signals of spectral photon-counting computerized X-ray tomography. Similarly to spin echo magnetic resonance tomography, the mathematical approach to spectral high performance computerized tomography is based on the isogeneously invariant, projectively weighted, symplectic coadjoint orbit picture Lie(N)∗/CoAd(N)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathfrak {Lie}(\\mathcal {N})^*/\ extrm{CoAd}(\\mathcal {N})$$\\end{document} of the real 3-dimensional Heisenberg unipotent Lie group N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {N}$$\\end{document} of type GL(3,R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{GL}(3,\\mathbb {R})$$\\end{document}, and its solvable diamond toric Lie group extension.

High transmission efficiency long-wave infrared multispectral modulation array based on nanogap engineering

The long-wave infrared spectrum’s unique advantages in molecular fingerprinting and atmospheric transmission have led to its widespread application in detection, identification, medical diagnosis, and environmental monitoring. Consequently, there has been a strong focus on developing multispectral structure arrays with high transmission efficiency. In this study, we introduce a high-transmission-efficiency nanocoaxes field enhancement structures array based on complex nanogap engineering, exhibiting a transmission of 21.59 % within an open area of 3 % and accompanied by a 90-fold enhancement of the nanogap field. With the modulation of nanogaps, the absolute transmission can exceed 60 %. The experimental data and finite element simulation results of the spectrally tunable array confirm that the origin of resonance is attributed to the enhanced localized electromagnetic modes supported by nanogaps. Furthermore, we demonstrated the proposed nanocoaxes field-enhancement structures in practical applications such as molecular resonance absorption enhancement and material composition analysis. Our work not only provides a method for on-chip multispectral tuning in the long-wave infrared range but also contributes to further advancing the development of long-wave infrared nanophotonic structures.

Second Order Bipolar Fuzzy Matrix and its Application in Medical Diagnosis

Second Order Bipolar Fuzzy Matrix and its Application in Medical Diagnosis

Innovations in ion-selective optodes: a comprehensive exploration of modern designs and nanomaterial integration.

In recent years, ion-selective optodes (ISOs) have remarkably progressed, driven by innovative modern designs and nanomaterial integration. This review explored the development of modern ISO by describing state-of-the-art strategies to improve their sensitivity, selectivity, and real-time monitoring capacity. The review reported the traditional membrane based-optodes, and investigated the latest research, current design principles, and the use of essential components, such as ionophores, indicator dyes, polymer membranes, and nanomaterials, in ISO fabrication. Special attention was given to nanomaterials (e.g., quantum dots, polymer dots, nanospheres, nanorods and nanocapsules) and particularly on how rare earth elements can further enhance their potential. It also described innovative ISO designs, including wearable optodes, smartphone-based optodes, and disposable paper-based optodes. As the pursuit of highly sensitive, selective, and adaptable ion sensing devices continues, this summary of the current knowledge sets the stage for upcoming innovations and applications in different domains (pharmaceutical formulations, medical diagnosis, environmental monitoring, and industrial applications).

An attribution graph-based interpretable method for CNNs

Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in various domains, such as face recognition, object detection, and image segmentation. However, the lack of transparency and limited interpretability inherent in CNNs pose challenges in fields such as medical diagnosis, autonomous driving, finance, and military applications. Several studies have explored the interpretability of CNNs and proposed various post-hoc interpretable methods. The majority of these methods are feature-based, focusing on the influence of input variables on outputs. Few methods undertake the analysis of parameters in CNNs and their overall structure. To explore the structure of CNNs and intuitively comprehend the role of their internal parameters, we propose an Attribution Graph-based Interpretable method for CNNs (AGIC) which models the overall structure of CNNs as graphs and provides interpretability from global and local perspectives. The runtime parameters of CNNs and feature maps of each image sample are applied to construct attribution graphs (At-GCs), where the convolutional kernels are represented as nodes and the SHAP values between kernel outputs are assigned as edges. These At-GCs are then employed to pretrain a newly designed heterogeneous graph encoder based on Deep Graph Infomax (DGI). To comprehensively delve into the overall structure of CNNs, the pretrained encoder is used for two types of interpretable tasks: (1) a classifier is attached to the pretrained encoder for the classification of At-GCs, revealing the dependency of At-GC’s topological characteristics on the image sample categories, and (2) a scoring aggregation (SA) network is constructed to assess the importance of each node in At-GCs, thus reflecting the relative importance of kernels in CNNs. The experimental results indicate that the topological characteristics of At-GC exhibit a dependency on the sample category used in its construction, which reveals that kernels in CNNs show distinct combined activation patterns for processing different image categories, meanwhile, the kernels that receive high scores from SA network are crucial for feature extraction, whereas low-scoring kernels can be pruned without affecting model performance, thereby enhancing the interpretability of CNNs.

Subwavelength Grating Cascaded Microring Resonator Biochemical Sensors with Record-High Sensitivity.

Photonic integrated circuit biochemical and biomedical sensors show promising applications in medical diagnosis, food security, healthcare, and environmental monitoring. Silicon-on-insulator subwavelength grating waveguides and cascaded microring resonator structures enhance photon-analyte interaction, offering superior sensing performance (higher sensitivity with lower limit of detection and larger free spectral range) compared to traditional strip and slot waveguide microring resonator structures. In this study, we design, simulate, and experimentally demonstrate a novel and compact biochemical sensor integrating subwavelength grating cascaded microring resonators and multibox subwavelength grating straight waveguides on a silicon-on-insulator platform. We achieve a record-high refractive index sensitivity of 810 nm/RIU with a limit of detection value of 2.04 × 10-5 RIU. The measured concentration sensitivity for sodium chloride solutions is 1430 pm/% with a limit of detection of 0.04%. The free spectral range is 35.8 nm, and the measured Q factor is 1.9 × 103. By combining the advantages of cascaded microring resonators with those subwavelength gratings, our sensor offers unprecedented sensitivity for biochemical sensing applications, promising significant enhancements in healthcare diagnostics and environmental monitoring systems.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.