Field Of Medical Imaging Research Articles

Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

OCT Images Diagnosis Based on Deep Learning – A Review

The recent advancements in deep learning technology have significantly transformed the field of medical imaging, namely in the diagnosis of ocular illnesses. The progress made in this field has improved the capacity to extract and evaluate intricate characteristics in images, with Optical Coherence Tomography (OCT) playing a crucial role. OCT has become known for its safe qualities and its high level of detail, rendering it an essential instrument in the diagnosis of eye diseases. The interesting improvement in research is centered around the integration of deep learning with OCT for the purpose of automating the detection of eye diseases. We conducted a comprehensive study that explores several diagnostic methods and the wide-ranging uses of OCT. Additionally, it addresses the accessibility of publicly available datasets that are specifically tailored to optical coherence tomography (OCT). The paper provides a detailed review of the most recent advancements in computer-assisted diagnostic methods for diseases of the eye, such as age-related macular degeneration, glaucoma, and diabetic macular edema, with a particular focus on the effective use of OCT. Moreover, the paper systematically analyzes the primary challenges that deep learning faces in OCT image interpretation, emphasizing the intricate nature and possibilities of this field.

A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives

This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring.

Solving the MPI reconstruction problem with automatically tuned regularization parameters

In the field of medical imaging, magnetic particle imaging (MPI) poses a promising non-ionizing tomographic technique with high spatial and temporal resolution. In MPI, iterative solvers are used to reconstruct the particle distribution out of the measured voltage signal based on a system matrix. The amount of regularization needed to reconstruct an image of good quality differs from measurement to measurement, depending on the MPI system and the measurement settings. Finding the right choice for the three major parameters controlling the regularization is commonly done by hand and requires time and experience. In this work, we study the reduction to a single regularization parameter and propose a method that enables automatic reconstruction. The method is qualitatively and quantitatively validated on several MPI data sets showing promising results.

MODERN INTELLECTUAL TECHNOLOGIES IN COMPUTER TOMOGRAPHY

In this article, an approach for the enhancement of computer tomography imaging by integrating modern technologies, including photon counting and dual-source CT, along with the application of the Ai-rad principles in Siemens Computer Tomography systems, is presented. The study proposes and empirically assesses the impact of these technologies on image quality and radiation safety. The architecture of dual-source CT and the photon-counting technique are explored, showcasing their role in improving diagnostic precision. Moreover, the article elaborates on the implementation of artificial intelligence through the Ai-rad approach, aimed at optimizing the scanning process and image analysis, while ensuring radiation doses are kept as low as reasonably achievable. The advantages of these contemporary technologies in addressing current challenges in CT imaging are presented. The results indicate that the synergy between advanced hardware capabilities and intelligent software algorithms leads to enhanced image resolution, reduced exposure to radiation, and overall improved clinical outcomes. This multi-faceted approach exemplifies the potential of modern innovations to set new benchmarks in the field of medical imaging. Keywords: Dual-Source CT, Photon Counting, AI-Rad, myNeedle, Diagnostic Precision, Radiation Safety.

Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS)

BackgroundMoyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies.MethodsThe MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients.ConclusionsThe MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.

Improving polygon image segmentation by enhancing U-Net architecture

The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model’s capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer.

Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness.

In this work, we address the task of few-shot medical image segmentation (MIS) with a novel proposed framework based on the learning registration to learn segmentation (LRLS) paradigm. To cope with the limitations of lack of authenticity, diversity, and robustness in the existing LRLS frameworks, we propose the better registration better segmentation (BRBS) framework with three main contributions that are experimentally shown to have substantial practical merit. First, we improve the authenticity in the registration-based generation program and propose the knowledge consistency constraint strategy that constrains the registration network to learn according to the domain knowledge. It brings the semantic-aligned and topology-preserved registration, thus allowing the generation program to output new data with great space and style authenticity. Second, we deeply studied the diversity of the generation process and propose the space-style sampling program, which introduces the modeling of the transformation path of style and space change between few atlases and numerous unlabeled images into the generation program. Therefore, the sampling on the transformation paths provides much more diverse space and style features to the generated data effectively improving the diversity. Third, we first highlight the robustness in the learning of segmentation in the LRLS paradigm and propose the mix misalignment regularization, which simulates the misalignment distortion and constrains the network to reduce the fitting degree of misaligned regions. Therefore, it builds regularization for these regions improving the robustness of segmentation learning. Without any bells and whistles, our approach achieves a new state-of-the-art performance in few-shot MIS on two challenging tasks that outperform the existing LRLS-based few-shot methods. We believe that this novel and effective framework will provide a powerful few-shot benchmark for the field of medical image and efficiently reduce the costs of medical image research. All of our code will be made publicly available online.

Investigation of medical image segmentation techniques and analysis of key applications

This research examines the application of the UNet convolutional neural network model, specifically for semantic segmentation tasks in the field of medical imaging, juxtaposing its efficacy with Fully Convolutional Networks (FCNs). The primary focus of this comparative analysis rests on the performance of the UNet model on the dataset employed for this study. Surpassing our initial expectations, the UNet model demonstrated remarkable performance superiority over the FCN model on the curated dataset, thereby suggesting its potential applicability and utility for analogous tasks within the realm of medical imaging. In a surprising turn of events, our trials revealed that data augmentation techniques did not usher in a notable enhancement in segmentation accuracy. This observation was especially striking given the substantial size of the dataset employed for the experiments, encompassing as many as 1000 images. This outcome suggests that the merits of data augmentation may not always come to the fore when dealing with considerably large datasets. This intriguing discovery prompts further exploration and investigation to uncover the underlying reasons behind this observed phenomenon. Moreover, it brings to light an open-ended research query - the quest for alternative methodologies that could potentially amplify segmentation accuracy when operating on large scale datasets in the sphere of medical imaging. As the field continues to evolve and mature, it is these open questions that will continue to push the boundaries of what is possible in medical image analysis.

An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases.

On the dynamics of soliton solutions for the nonlinear fractional dynamical system: Application in ultrasound imaging

This article examines the third-order non-linear fractional Westervelt model by using a newly developed integration tool termed the new extended direct algebraic approach. The different wave structures to the considered model are secured in various forms, including bright, dark, and combo solitons. The application of wave structures is advantageous in the examination of sound wave propagation and high amplitude phenomena in the fields of medical imaging and therapy. These solutions are effective in facilitating ultrasound propagation in tissue, underwater acoustics, acoustic cavitation, acoustic levitation, and other related applications. The internal tissue of human beings can be visualized and studied through the use of ultrasound imaging technologies in the field of medicine. This technology possesses numerous uses in both industrial and medicinal sectors. There has been a growing interest in fractional nonlinear partial differential equations due to their ability to describe various complex occurrences and exhibit more dynamic architectures of localized wave solutions. On employing accurate parameter values, multiple graphical representations are generated to provide the visual depiction of the acquired results. The results of this study indicate that the chosen methodology effectively improves nonlinear dynamical processes. The findings indicate that the selected methodology demonstrates efficacy, feasibility, and versatility when applied to intricate systems across several domains, with a special emphasis on ultrasonic imaging. The findings indicate that the system exhibits a potentially significant abundance of soliton structures.

Thermally Activated Delayed Fluorescence (TADF)-active Coinage-metal Sulfide Clusters for High-resolution X-ray Imaging.

The study of facile-synthesis and low-cost X-ray scintillators with high light yield, low detection limit and high X-ray imaging resolution plays a vital role in medical and industrial imaging fields. However, the optimal balance between X-ray absorption, decay lifetime and excitonic utilization efficiency of scintillators to achieve high-resolution imaging is extremely difficult due to the inherent contradiction. Here two thermally activated delayed fluorescence (TADF)-actived coinage-metal clusters M6 S6 L6 (M=Ag or Cu) were synthesized by simple solvothermal reaction, where the cooperation of heavy atom-rich character and TADF mechanism supports strong X-ray absorption and rapid luminescent collection of excitons. Excitingly, Ag6 S6 L6 (SC-Ag) displays a high photoluminescence quantum yield of 91.6 % and scintillating light yield of 17420 photons MeV-1 , as well as a low detection limit of 208.65 nGy s-1 that is 26 times lower than the medical standard (5.5 μGy s-1 ). More importantly, a high X-ray imaging resolution of 16 lp/mm based on SC-Ag screen is demonstrated. Besides, rigid core skeleton reinforced by metallophilicity endows clusters M6 S6 L6 strong resistance to humidity and radiation. This work provides a new view for the design of efficient scintillators and opens the research door for silver clusters in scintillation application.

Thermally Activated Delayed Fluorescence (TADF)‐active Coinage‐metal Sulfide Clusters for High‐resolution X‐ray Imaging

AbstractThe study of facile‐synthesis and low‐cost X‐ray scintillators with high light yield, low detection limit and high X‐ray imaging resolution plays a vital role in medical and industrial imaging fields. However, the optimal balance between X‐ray absorption, decay lifetime and excitonic utilization efficiency of scintillators to achieve high‐resolution imaging is extremely difficult due to the inherent contradiction. Here two thermally activated delayed fluorescence (TADF)‐actived coinage‐metal clusters M6S6L6 (M=Ag or Cu) were synthesized by simple solvothermal reaction, where the cooperation of heavy atom‐rich character and TADF mechanism supports strong X‐ray absorption and rapid luminescent collection of excitons. Excitingly, Ag6S6L6 (SC‐Ag) displays a high photoluminescence quantum yield of 91.6 % and scintillating light yield of 17420 photons MeV−1, as well as a low detection limit of 208.65 nGy s−1 that is 26 times lower than the medical standard (5.5 μGy s−1). More importantly, a high X‐ray imaging resolution of 16 lp/mm based on SC‐Ag screen is demonstrated. Besides, rigid core skeleton reinforced by metallophilicity endows clusters M6S6L6 strong resistance to humidity and radiation. This work provides a new view for the design of efficient scintillators and opens the research door for silver clusters in scintillation application.

COVID-19 Detection and Diagnosis Model on CT Scans Based on AI Techniques.

The end of 2019 could be mounted in a rudimentary framing of a new medical problem, which globally introduces into the discussion a fulminant outbreak of coronavirus, consequently spreading COVID-19 that conducted long-lived and persistent repercussions. Hence, the theme proposed to be solved arises from the field of medical imaging, where a pulmonary CT-based standardized reporting system could be addressed as a solution. The core of it focuses on certain impediments such as the overworking of doctors, aiming essentially to solve a classification problem using deep learning techniques, namely, if a patient suffers from COVID-19, viral pneumonia, or is healthy from a pulmonary point of view. The methodology's approach was a meticulous one, denoting an empirical character in which the initial stage, given using data processing, performs an extraction of the lung cavity from the CT scans, which is a less explored approach, followed by data augmentation. The next step is comprehended by developing a CNN in two scenarios, one in which there is a binary classification (COVID and non-COVID patients), and the other one is represented by a three-class classification. Moreover, viral pneumonia is addressed. To obtain an efficient version, architectural changes were gradually made, involving four databases during this process. Furthermore, given the availability of pre-trained models, the transfer learning technique was employed by incorporating the linear classifier from our own convolutional network into an existing model, with the result being much more promising. The experimentation encompassed several models including MobileNetV1, ResNet50, DenseNet201, VGG16, and VGG19. Through a more in-depth analysis, using the CAM technique, MobilneNetV1 differentiated itself via the detection accuracy of possible pulmonary anomalies. Interestingly, this model stood out as not being among the most used in the literature. As a result, the following values of evaluation metrics were reached: loss (0.0751), accuracy (0.9744), precision (0.9758), recall (0.9742), AUC (0.9902), and F1 score (0.9750), from 1161 samples allocated for each of the three individual classes.

UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset."

Achromatic polarization-multiplexed varifocal metalens with a broad bandwidth

A polarization-multiplexed imaging system that utilises metasurfaces as its design effectively addresses the issue of the conventional imaging system's cumbersome size. However, their inability to function at broad wavelength due to the presence of chromatic aberration. An achromatic polarization-multiplexed varifocal metalens with a broad bandwidth (APVM) is illustrated in this paper. By utilising a particle swarm optimization algorithm, we have significantly diminished the phase compensation matching error in the achromatic scheme. Simulation results show that the design can effectively suppress the chromatic aberration at 610–740 nm wavelengths and change the focal length according to the polarization. Furthermore, functioning as an achromatic polarization-multiplexed metalens, demonstrates an exceptional average focusing efficiency of 49.7 % and nearly diffraction-limited focusing across the operational wavelengths. It is anticipated that this design will find widespread applications in the fields of medical imaging, target detection and optical communications.

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis

BackgroundAccurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.MethodsSearches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386.ResultsThirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83–91%), specificity of 94% (92–96%), and AUC of 97% (95–98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or > 200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia).ConclusionsEven if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.

Anatomical sites identification in both ordinary and capsule gastroduodenoscopy via deep learning

Anatomical sites recognition is a basic requirement for gastroenterologists. But there is not a unified framework for anatomical sites identification in both ordinary and capsule endoscopes. Deep learning (DL), especially vision transformer (ViT), is promising in the field of medical imaging, however, the performance of them is not comprehensively compared. The retrospective cohort study included 322 patients who visited Friendship hospital for capsule endoscopy and 556 patients who visited Minhang hospital for ordinary endoscopy. Convolutional neural network (CNN) and two types of ViT (B/16 and L/32) that was trained to identify qualified and low-quality images (the first model), then the qualified images were used to train the second model for distinguishing different anatomical sites. 62,850 images from capsule endoscopy and 17,434 images from ordinary endoscopy were included in developing models. In internal cross-validation, CNN achieved average area under receiver operating characteristic curve (AUROC) of 0.9844 (95 % confidence interval [CI] 0.9640–0.9960) in distinguishing qualified and low-quality images, and 0.9251 average accuracy (95 % CI 0.9133–0.9369) in distinguishing different anatomical sites. Besides, the performance of ViT did not surpass the performance of CNN. 18,636 images from 355 patients who received capsule endoscopy and 15,949 images of 501 patients who received ordinary endoscopy were prospectively collected. The AUROC of CNN reached 0.8715 (95 % CI 0.8674–0.8754) in the first model, and 0.8376 accuracy (95 % CI 0.8336–0.8414) for the second model, respectively. The performance of CNN is better than ViT with the same hyperparameter setting for sifting out the unqualified images and distinguishing anatomical sites effectively.

Advancements in Diagnostic Imaging Techniques for Early Cancer Detection

The field of medical imaging has evolved at a blinding pace, and recent developments in molecular imaging have sparked significant interest in the healthcare industry. Cancer is a leading cause of mortality across the globe, and this review explores advancements in diagnostic imaging techniques that facilitate early cancer detection. The review further highlights that although traditional techniques, computed tomography and magnetic resonance imaging, have improved the diagnostic abilities and patient outcomes, they still face limitations such as time constraints and radiation exposure. Comparatively, cutting-edge modalities, such as molecular imaging and hybrid technology, offer enhanced sensitivity and specificity to ensure a more favorable prognosis for patients. Future research must revolve around the integration of artificial intelligence into medical imaging to develop personalized treatment plans, paving the way for improved accuracy and more effective cancer diagnosis and treatment.

Efforts of Neutrosophic Logic in Medical Image Processing and Analysis

Medical image processing is indispensable for correct diagnosis and planning of treatment. However, it is susceptible to many errors due to noise, artifacts, and the variability innate in anatomical structures themselves. Traditional image analysis methods hence suffer from these complexities in the images themselves and lead to probable inaccuracies in image analysis. This paper probes into the role of neutrosophic logic in the domain of medical image processing to seek better handling of these problems. The main objectives of the work were to optimize the noise reduction, image segmentation, feature extraction, and classification using the special capabilities of neutrosophic logic directed toward handling uncertainty and indeterminacy. Contributions The contributions of this study are multifaceted: it contributes by introducing detailed support for applying neutrosophic logic in a number of medical image processing tasks and integrates neutrosophic logic with prior techniques and evaluates their performance with traditional methods. The experimental results in the study are complete and demonstrate significant improvements in key metrics. For example, applying neutrosophic logic in noise reduction increased the peak signal-to-noise ratio of MRI images from 25 dB to 35 dB. In some segmentation tasks, the Dice coefficient for liver CT scans increased from 0.85 to 0.92. It increases the accuracy of feature extraction in breast cancer detection from 88% to 95%, while integrating neutrosophic logic with convolutional neural networks improves the accuracy in retinal image classification from 92% to 97%. All these results underline the strong role that neutrosophic logic can play in enhancing accuracy, robustness, and reliability in the processing of medical images. The result of the study concludes that neutrosophic logic not only improves the current limitations but also holds great promise for handling uncertainty in many medical fields, opening a promising way for future advancements in the field of medical imaging and health applications.