Hasil untuk "Diseases of the endocrine glands. Clinical endocrinology"

Ane G Domingo-Aldama, Marcos Merino Prado, Alain García Olea et al.

This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses discharge reports to support the three main steps of early prediction: cohort selection, dataset generation, and outcome labeling. By processing discharge reports with natural language processing techniques, we can efficiently identify relevant patient cohorts, enrich structured datasets with additional clinical variables, and generate high-quality labels without manual intervention. This approach addresses the frequent issue of missing or incomplete data in codified electronic health records (EHR), capturing clinically relevant information that is often underrepresented. We evaluate the methodology in the context of predicting atrial fibrillation (AF) progression, showing that predictive models trained on datasets enriched with discharge report information achieve higher accuracy and correlation with true outcomes compared to models trained solely on structured EHR data, while also surpassing traditional clinical scores. These results demonstrate that automating the integration of unstructured clinical text can streamline early prediction studies, improve data quality, and enhance the reliability of predictive models for clinical decision-making.

Chin-Chou Yang, Wen-Chen Ouyang, Tsuo-Hung Lan et al.

Conclusions: The P4P program was associated with significantly improved glycemic control in psychiatric patients with diabetes compared to usual care. This integrated care model may be an effective strategy to improve diabetes outcomes in psychiatric populations.

Yuri Gardinazzi, Roger Gonzaléz March, Suprabhath Kalahasti et al.

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.

Fahad Salman, Abhisri Ramesh, Thomas Jochmann et al.

Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified. Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting. Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time. Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity. Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes.

Jeanny Pan, Philipp Seeböck, Christoph Fürböck et al.

Identifying new disease-related patterns in medical imaging data with the help of machine learning enlarges the vocabulary of recognizable findings. This supports diagnostic and prognostic assessment. However, image appearance varies not only due to biological differences, but also due to imaging technology linked to vendors, scanning- or re- construction parameters. The resulting domain shifts impedes data representation learning strategies and the discovery of biologically meaningful cluster appearances. To address these challenges, we introduce an approach to actively learn the domain shift via post-hoc rotation of the data latent space, enabling disentanglement of biological and technical factors. Results on real-world heterogeneous clinical data showcase that the learned disentangled representation leads to stable clusters representing tissue-types across different acquisition settings. Cluster consistency is improved by +19.01% (ARI), +16.85% (NMI), and +12.39% (Dice) compared to the entangled representation, outperforming four state-of-the-art harmonization methods. When using the clusters to quantify tissue composition on idiopathic pulmonary fibrosis patients, the learned profiles enhance Cox survival prediction. This indicates that the proposed label-free framework facilitates biomarker discovery in multi-center routine imaging data. Code is available on GitHub https://github.com/cirmuw/latent-space-rotation-disentanglement.

Xiao-li Li, Yan-fei Ji, Yu Feng et al.

Objective To investigate the differences in the metabolic indicators and sex hormones between obese and non-obese patients with polycystic ovary syndrome (PCOS), and their impacts on endometrial receptivity (ER).Methods We selected 255 individuals with PCOS, and categorized them into the obese groups, including the OP group (obese patients with PCOS) and the ON group (obese patients without PCOS), and selected 64 individuals who were categorized in the non-obese groups, namely, the control groups, which comprise the NP group (non-obese patients with PCOS) and the NN group(non-obese patients without PCOS). The one-way analysis of variance (ANOVA) and Mann-Whitney U tests were used to compare the metabolic indicators, and sex hormone-associated and ER-associated indicators between the groups. The correlation between the aforementioned clinical markers and ER was analyzed using the Pearson’s correlation coefficient.Results (1) In comparison with the NP group, the OP group exhibited higher levels (p < .01) of free androgen index (FAI), anti-müllerian hormone (AMH), fasting insulin (FINS), insulin level within 60 min, 120 min, and 180 min—60minINS, 120minINS, and 180minINS, respectively, fasting blood glucose (FBG), blood glucose level within two hours (2hGlu), homeostatic model assessment for insulin resistance (HOMA-IR), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), waist-to-hip ratio (WHR), waist circumference, hip circumference, the ratio of the maximum blood flow velocity of the uterine artery during systole to the blood flow velocity of the uterine artery at the end of diastole (uterine artery S/D), and blood flow resistance index (RI) of the uterine artery. In comparison with the NP group, the OP group exhibited lower levels (p < .01) of sex hormone binding globulin (SHBG), dehydroepiandrosterone (DHEA), high molecular weight adiponectin (HMWA), and high-density lipoprotein cholesterol (HDL-C). (2) In the PCOS group, RI was significantly positively correlated with FAI, FINS, 120minINS, HOMA-IR, and WHR (p < .01), and significantly negatively correlated with SHBG, HDL-C, and HMWA (p < .01); uterine artery S/D was significantly positively correlated with FAI, FINS, 2hGlu, HOMA-IR, LDL-C, and WHR (p < .01), significantly positively correlated with 120minINS and FBG (p < .05), and significantly negatively correlated with SHBG and HMWA (p < .01).Conclusion (1) The OP group exhibited obvious metabolic disorders and poor ER, which was manifested as low levels of SHBG and HMWA, and high levels of FAI, HOMA-IR, WHR, uterine artery S/D, and RI. (2) In patients with PCOS, there was a substantial correlation between ER-associated indicators RI and uterine artery S/D and FAI, FINS, 120minINS, HOMA-IR, WHR, SHBG, and HMWA.

Abul Hasan, Jinge Wu, Quang Ngoc Nguyen et al.

This study introduces a novel knowledge enhanced tokenisation mechanism, K-Tokeniser, for clinical text processing. Technically, at initialisation stage, K-Tokeniser populates global representations of tokens based on semantic types of domain concepts (such as drugs or diseases) from either a domain ontology like Unified Medical Language System or the training data of the task related corpus. At training or inference stage, sentence level localised context will be utilised for choosing the optimal global token representation to realise the semantic-based tokenisation. To avoid pretraining using the new tokeniser, an embedding initialisation approach is proposed to generate representations for new tokens. Using three transformer-based language models, a comprehensive set of experiments are conducted on four real-world datasets for evaluating K-Tokeniser in a wide range of clinical text analytics tasks including clinical concept and relation extraction, automated clinical coding, clinical phenotype identification, and clinical research article classification. Overall, our models demonstrate consistent improvements over their counterparts in all tasks. In particular, substantial improvements are observed in the automated clinical coding task with 13\% increase on Micro $F_1$ score. Furthermore, K-Tokeniser also shows significant capacities in facilitating quicker converge of language models. Specifically, using K-Tokeniser, the language models would only require 50\% of the training data to achieve the best performance of the baseline tokeniser using all training data in the concept extraction task and less than 20\% of the data for the automated coding task. It is worth mentioning that all these improvements require no pre-training process, making the approach generalisable.

Abigail Burdon, Thomas Jaki, Xijin Chen et al.

Background: Drug development is often inefficient, costly and lengthy, yet it is essential for evaluating the safety and efficacy of new interventions. Compared with other disease areas, this is particularly true for Phase II / III cancer clinical trials where high attrition rates and reduced regulatory approvals are being seen. In response to these challenges, seamless clinical trials and master protocols have emerged to streamline the drug development process. Methods: Seamless clinical trials, characterized by their ability to transition seamlessly from one phase to another, can lead to accelerating the development of promising therapies while Master protocols provide a framework for investigating multiple treatment options and patient subgroups within a single trial. Results: We discuss the advantages of these methods through real trial examples and the principals that lead to their success while also acknowledging the associated regulatory considerations and challenges. Conclusion: Seamless designs and Master protocols have the potential to improve confirmatory clinical trials. In the disease area of cancer, this ultimately means that patients can receive life-saving treatments sooner.

Chun-Hsiao Yeh, Jiayun Wang, Andrew D. Graham et al.

Accurate diagnosis of ocular surface diseases is critical in optometry and ophthalmology, which hinge on integrating clinical data sources (e.g., meibography imaging and clinical metadata). Traditional human assessments lack precision in quantifying clinical observations, while current machine-based methods often treat diagnoses as multi-class classification problems, limiting the diagnoses to a predefined closed-set of curated answers without reasoning the clinical relevance of each variable to the diagnosis. To tackle these challenges, we introduce an innovative multi-modal diagnostic pipeline (MDPipe) by employing large language models (LLMs) for ocular surface disease diagnosis. We first employ a visual translator to interpret meibography images by converting them into quantifiable morphology data, facilitating their integration with clinical metadata and enabling the communication of nuanced medical insight to LLMs. To further advance this communication, we introduce a LLM-based summarizer to contextualize the insight from the combined morphology and clinical metadata, and generate clinical report summaries. Finally, we refine the LLMs' reasoning ability with domain-specific insight from real-life clinician diagnoses. Our evaluation across diverse ocular surface disease diagnosis benchmarks demonstrates that MDPipe outperforms existing standards, including GPT-4, and provides clinically sound rationales for diagnoses.

Kelsey Detels, David Shin, Harrison Wilson et al.

Plantar pressure measurements can provide valuable insight into various health characteristics in patients. In this study, we describe different plantar pressure devices available on the market and their clinical relevance. Current devices are either platform-based or wearable and consist of a variety of sensor technologies: resistive, capacitive, piezoelectric, and optical. The measurements collected from any of these sensors can be utilized for a range of clinical applications including patients with diabetes, trauma, deformity and cerebral palsy, stroke, cervical myelopathy, ankle instability, sports injuries, and Parkinsons disease. However, the proper technology should be selected based on the clinical need and the type of tests being performed on the device. In this review we provide the reader with a simple overview of the existing technologies their advantages and disadvantages and provide application examples for each. Moreover, we suggest new areas in orthopaedic that plantar pressure mapping technology can be utilized for increased quality of care.

Oleksandr Sorochynskyi, Quentin Guibert, Frédéric Planchet et al.

The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods and data to conduct this evaluation vary considerably in nature and quality. Traditionally, health data collection relies on population surveys. However, these studies, typically of limited size, encompass only a small yet representative segment of the population. This limitation can necessitate the separate estimation of incidence and mortality rates, significantly restricting the available analysis methods. In this article, we leverage an extract from the French National Hospital Discharge database to define health indicators. Our analysis focuses on the resulting Disease-Free Life Expectancy (Dis-FLE) indicator, which provides insights based on the hospital trajectory of each patient admitted to hospital in France during 2008-13. Through this research, we illustrate the advantages and disadvantages of employing large clinical datasets as the foundation for more robust health indicators. We shed light on the opportunities that such data offer for a more comprehensive understanding of the health status of a population. In particular, we estimate age-dependent hazard rates associated with sex, alcohol abuse, tobacco consumption, and obesity, as well as geographic location. Simultaneously, we delve into the challenges and limitations that arise when adopting such a data-driven approach.

Bimarsha Khanal, Paras Poudel, Anish Chapagai et al.

Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.

Yijing Yang, Vasileios Magoulianitis, Jiaxin Yang et al.

Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is proposed, named PSHop. Different from deep learning based methods, the core methodology of PSHop is a feed-forward encoder-decoder system based on successive subspace learning (SSL). It consists of two modules: 1) encoder: fine to coarse unsupervised representation learning with cascaded VoxelHop units, 2) decoder: coarse to fine segmentation prediction with voxel-wise classification and local refinement. Experiments are conducted on the publicly available ISBI-2013 dataset, as well as on a larger private one. Experimental analysis shows that our proposed PSHop is effective, robust and lightweight in the tasks of prostate gland and zonal segmentation, achieving a Dice Similarity Coefficient (DSC) of 0.873 for the gland segmentation task. PSHop achieves a competitive performance comparatively to other deep learning methods, while keeping the model size and inference complexity an order of magnitude smaller.

Chu Myaet Thwal, Kyi Thar, Ye Lin Tun et al.

Health management has become a primary problem as new kinds of diseases and complex symptoms are introduced to a rapidly growing modern society. Building a better and smarter healthcare infrastructure is one of the ultimate goals of a smart city. To the best of our knowledge, neural network models are already employed to assist healthcare professionals in achieving this goal. Typically, training a neural network requires a rich amount of data but heterogeneous and vulnerable properties of clinical data introduce a challenge for the traditional centralized network. Moreover, adding new inputs to a medical database requires re-training an existing model from scratch. To tackle these challenges, we proposed a deep learning-based clinical decision support system trained and managed under a federated learning paradigm. We focused on a novel strategy to guarantee the safety of patient privacy and overcome the risk of cyberattacks while enabling large-scale clinical data mining. As a result, we can leverage rich clinical data for training each local neural network without the need for exchanging the confidential data of patients. Moreover, we implemented the proposed scheme as a sequence-to-sequence model architecture integrating the attention mechanism. Thus, our objective is to provide a personalized clinical decision support system with evolvable characteristics that can deliver accurate solutions and assist healthcare professionals in medical diagnosing.

Zhufeng Chen, Zhufeng Chen, Yaqian Mao et al.

ObjectiveMedullary thyroid carcinoma (MTC) patients with distant metastases frequently present a relatively poor survival prognosis. Our main purpose was developing a nomogram model to predict distant metastases in MTC patients.MethodsThis was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database. Data of 807 MTC patients diagnosed from 2004 to 2015 who undergone total thyroidectomy and neck lymph nodes dissection was included in our study. Independent risk factors were screened by univariate and multivariate logistic regression analysis successively, which were used to develop a nomogram model predicting for distant metastasis risk. Further, the log‐rank test was used to compare the differences of Kaplan-Meier curves of cancer-specific survival (CSS) in different M stage and each independent risk factor groups.ResultsFour clinical parameters including age &gt; 55 years, higher T stage (T3/T4), higher N stage (N1b) and lymph node ratio (LNR) &gt; 0.4 were significant for distant metastases at the time of diagnosis in MTC patients, and were selected to develop a nomogram model. This model had satisfied discrimination with the AUC and C-index of 0.894, and C-index was confirmed to be 0.878 through bootstrapping validation. A decision curve analysis (DCA) was subsequently made to evaluate the feasibility of this nomogram for predicting distant metastasis. In addition, CSS differed by different M stage, T stage, N stage, age and LNR groups.ConclusionsAge, T stage, N stage and LNR were extracted to develop a nomogram model for predicting the risk of distant metastases in MTC patients. The model is of great significance for clinicians to timely identify patients with high risk of distant metastases and make further clinical decisions.

Fashun Liu, Yue Li, Zhenxiong Ye et al.

IntroductionObesity-related ovulation abnormalities (OA) affect fertility. LSG is the most frequent bariatric operation. However, no research has identified a reliable indicator for predicting OA recovery after LSG. The purpose of this research was to examine the prognostic usefulness of preoperative the luteinizing hormone (LH) to follicle-stimulating hormone (FSH) ratio (LFR).MethodsOur department conducted a prospective study from 2016 to 2021. Venous blood was typically tested 3 days before surgery to get the preoperative LFR. Descriptive data, preoperative and postoperative variables were also collected. Binary logistic regression related preoperative LFR with OA recovery. The receiver operating characteristic (ROC) curve evulated preoperative LFR’s predictive capability.ResultsA total of 157 women with a complete follow-up of one year were included. LFR was the only factor linked with OA (P &lt; 0.001). AUC (area under the ROC curve) = 0.915, cutoff = 1.782, sensitivity = 0.93, and specificity = 0.82.DiscussionOverall, LSG has a favorable surgical result, with a %TWL of 66.082 ± 12.012 at 12 months postoperatively. Preoperative sexual hormone levels, as expressed by LFR, has the potential to predict the fate of OA following LSG at one year post-operatively.

Samuel Cortez, Ana Maria Arbeláez, Michael Wallendorf et al.

INTRODUCTION: Current peak serum cortisol cutoffs for the diagnosis of adrenal insufficiency (AI) after Cosyntropin stimulation have been established using polyclonal antibody (pAb) immunoassays. However, new and highly specific cortisol monoclonal antibody (mAb) immunoassays are being used more widely, which can potentially yield higher false positive rates. Thus, this study aimed to redefine the biochemical diagnostic cutoff points for AI in children when using a highly specific cortisol mAb immunoassay and liquid chromatography tandem mass spectrometry (LC/MS) to avoid unnecessary steroid use. METHODS: Cortisol levels from 36 children undergoing 1 mcg Cosyntropin stimulation tests to rule out AI were measured using pAb immunoassay (Roche Elecsys Cortisol I), mAB immunoassay (Roche Elecsys Cortisol II), and LC/MS. Logistic regression was used to predict AI using the pAB as the reference standard. A receiver operator characteristic curve, area under the curve (AUC), sensitivity, specificity, and kappa agreement were also calculated. RESULTS: Using a peak serum cortisol cutoff value of 12.5 μg/dL for the mAb immunoassay provided 99% sensitivity and 94% specificity for diagnosing AI, when compared to the historical pAb immunoassay cutoff of 18 μg/dL (AUC=0.997). Likewise, a cutoff of value of 14 μg/dL using the LC/MS, provided 99% sensitivity and 88% specificity when compared to the pAb immunoassay (AUC=0.995). DISCUSSION AND CONCLUSION: To prevent overdiagnosis of AI in children undergoing 1 mcg Cosyntropin stimulation test, our data support using a new peak serum cortisol cutoff of 12.5 μg/dL and 14 μg/dL to diagnose AI when using mAb immunoassays and LC/MS in children, respectively.

Barre Douglas E., Mizier-Barre Kazimiera A., Griscti Odette et al.

Objective. The intent of the present study was to test two hypotheses. The primary hypothesis was that there would be differences between blood serum individual free fatty acids (SIFFA) and serum individual total fatty acids (SITFA) in terms of their different relationships (correlations) to each of homeostatic model assessment-individual insulin resistance (HOMA-IR) and homeostatic model assessment-individual insulin resistance-percentage β-cell function (HOMA-% β) remaining in human type 2 diabetic patients with pre-flaxseed oil (FXO) and pre-safflower oil (SFO) administration. The secondary hypothesis was that FXO (rich in alpha-linolenic acid, ALA) supplementation would alter these correlations differently in the SIFFA and STIFFA pools in comparison with the placebo SFO (poor in ALA).

Alhaam Alariyibi, Mohamed El-Jarai, Abdelsalam Maatuk

The healthcare industry generates enormous amounts of complex clinical data that make the prediction of disease detection a complicated process. In medical informatics, making effective and efficient decisions is very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely, J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the impact of the feature selection method. A comparative and analysis study was performed to determine the best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity and specificity. The importance of using classification techniques for heart disease diagnosis has been highlighted. We also reduced the number of attributes in the dataset, which showed a significant improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart disease was Random Forest with an accuracy of 99.24%.

Zeynep Hilal Kilimci, Mustafa Yalcin, Ayhan Kucukmanisa et al.

Heart disease, also known as cardiovascular disease, is a prevalent and critical medical condition characterized by the impairment of the heart and blood vessels, leading to various complications such as coronary artery disease, heart failure, and myocardial infarction. The timely and accurate detection of heart disease is of paramount importance in clinical practice. Early identification of individuals at risk enables proactive interventions, preventive measures, and personalized treatment strategies to mitigate the progression of the disease and reduce adverse outcomes. In recent years, the field of heart disease detection has witnessed notable advancements due to the integration of sophisticated technologies and computational approaches. These include machine learning algorithms, data mining techniques, and predictive modeling frameworks that leverage vast amounts of clinical and physiological data to improve diagnostic accuracy and risk stratification. In this work, we propose to detect heart disease from ECG images using cutting-edge technologies, namely vision transformer models. These models are Google-Vit, Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the initial endeavor concentrating on the detection of heart diseases through image-based ECG data by employing cuttingedge technologies namely, transformer models. To demonstrate the contribution of the proposed framework, the performance of vision transformer models are compared with state-of-the-art studies. Experiment results show that the proposed framework exhibits remarkable classification results.