Medical Imaging Applications Research Articles

Interpretable deep learning analysis on correction of motion blur X-ray images to ensure the efficiency and reliability of clinical decisions

Motion blur X-rays often lead to repeated imaging and extra radiation exposure. This study introduces an interpretable deep learning method to ensure AI prediction efficiency and reliability and uses the correction of motion blur X-ray images as a case study. A deep learning model that integrates Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) and EfficientNet-B3 is trained to correct motion-blurred images with interpretable analysis. ESRGAN corrects motion-blurred images and EfficientNet-B3 tackles the disease recognition. The accuracy on public datasets reaches 0.97 and clinical motion blur X-rays improves from 0.4 to 0.74 with an 85% increase. The results are analyzed using image quality metrics and interpretative methods to confirm the effectiveness and reliability of the proposed method. The proposed method ensures accuracy and reliability in disease recognition and improves the quality of X-ray images, which are verified with actual clinical data. This approach alleviates the workload of radiologists, reduces radiation exposure risks for patients, and holds promise for wider applications in medical imaging. To the best of our knowledge, this is the first study combining deep learning with interpretability to ensure the deblurring task of medical images with potential applications in CT and MRI.

Fabrication of elliptical-silica microfiber for ultrasound detection.

Elliptical shape microfiber enables many higher order modes compared with a circular microfiber. The small difference in the optical path length among many modes enabled multi-resonance peaks with high contrast in Mach-Zehnder (MZI) interferometers, which allows a large dynamic range and minimum detection sensitivity for broadband ultrasound sensing. In this paper, we present the design and fabrication of an ultra-compact elliptical-silica microfiber utilizing off-axis flame-drawing for ultrasound detection. The narrow transmission peak showed high contrast for ultrasensitive ultrasound wave detection. With a major-axis diameter of 6.25 µm, the elliptical-silica microfiber sensor exhibits a broadband ultrasound frequency response spanning from 20 kHz to 38.5 MHz. Furthermore, it achieves a signal-to-noise ratio (SNR) of up to 80 dB at 1 MHz, which is the resonance frequency of the microfiber and the linear response under driving voltages of 3-10 V for the PZT ultrasound generator. This low-cost microfiber sensor offers exceptional sensitivity across a broad ultrasonic bandwidth response, making it an ideal choice for nondestructive testing (NDT) and medical imaging applications. Its compact size and immunity to electric and magnetic fields further enhance its utility in various environments.

High-sensitivity fiber-tip acoustic sensor with ultrathin gold diaphragm

Miniature acoustic sensors with high sensitivity are highly desired for applications in medical photoacoustic imaging, acoustic communications and industrial nondestructive testing. However, conventional acoustic sensors based on piezoelectric, piezoresistive and capacitive detectors usually require a large element size on a millimeter to centimeter scale to achieve a high sensitivity, greatly limiting their spatial resolution and the application in space-confined sensing scenarios. Herein, by using single-crystal two-dimensional gold flakes (2DGFs) as the sensing diaphragm of an extrinsic Fabry-Perot interferometer on a fiber tip, we demonstrate a miniature optical acoustic sensor with high sensitivity. Benefiting from the ultrathin thickness (∼8 nm) and high reflectivity of the 2DGF, the fiber-tip acoustic sensor gives an acoustic pressure sensitivity of ∼300 mV/Pa in the frequency range from 100 Hz to 20 kHz. The noise-equivalent pressure of the fiber-tip acoustic sensor at the frequency of 13 kHz is as low as 62.8 µPa/Hz1/2, which is one or two orders of magnitude lower than that of reported optical acoustic sensors with the same size.

Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications

Artificial intelligence (AI) has emerged as a promising avenue for enhancing medical imaging systems and improving clinical workflows. This research explores innovative applications of AI and deep learning for image communication networks in healthcare. Specifically, we develop an intelligent image compression framework that optimizes data transmission and speeds interpretation of radiology scans. Our approach combines convolutional neural networks, generative adversarial networks, and specialized image filters to balance communication efficiency, diagnostic accuracy, and system latency. Rigorous experiments validate superior performance over traditional methods and commercial products across modalities including MRI, CT, and ultrasound. Crucially, the proposed methods demonstrate expert-level precision in anatomy labeling and pathology detection. By intelligently streamlining image transfer and analytics, this AI-powered system could facilitate ubiquitous, real-time diagnostics via telemedicine. Enhanced connectivity between imaging devices and clinical specialists can improve patient outcomes and reduce healthcare costs. Our solutions set the stage for more advanced AI integration in imaging networks and data-intensive medicine

Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers.

Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCTimages. To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinicalapplications. The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testingpurposes. The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results ofCBCT. We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.

CAFES: Chest X-ray Analysis using Federated Self-supervised Learning for Pediatric COVID-19 Detection.

Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.

Anatomic attention regions via optimal anatomy modeling and recognition for DL-based image segmentation.

Organ segmentation is a crucial task in various medical imaging applications. Many deep learning models have been developed to do this, but they are slow and require a lot of computational resources. To solve this problem, attention mechanisms are used which can locate important objects of interest within medical images, allowing the model to segment them accurately even when there is noise or artifact. By paying attention to specific anatomical regions, the model becomes better at segmentation. Medical images have unique features in the form of anatomical information, which makes them different from natural images. Unfortunately, most deep learning methods either ignore this information or do not use it effectively and explicitly. Combined natural intelligence with artificial intelligence, known as hybrid intelligence, has shown promising results in medical image segmentation, making models more robust and able to perform well in challenging situations. In this paper, we propose several methods and models to find attention regions in medical images for deep learning-based segmentation via non-deep-learning methods. We developed these models and trained them using hybrid intelligence concepts. To evaluate their performance, we tested the models on unique test data and analyzed metrics including false negatives quotient and false positives quotient. Our findings demonstrate that object shape and layout variations can be explicitly learned to create computational models that are suitable for each anatomic object. This work opens new possibilities for advancements in medical image segmentation and analysis.

A physics based machine learning model to characterize room temperature semiconductor detectors in 3D

Room temperature semiconductor radiation detectors (RTSD) for X-ray and γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma$$\\end{document}-ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades with desired detection properties. In a typical pixelated configuration, CZT have electrodes on opposite ends. For advanced event reconstruction algorithms at sub-pixel level, detailed characterization of the RTSD is required in three dimensional (3D) space. However, 3D characterization of the material defects and charge transport properties in the sub-pixel regime is a labor intensive process with skilled manpower and novel experimental setups. Presently, state-of-art characterization is done over the bulk of the RTSD considering homogenous properties. In this paper, we propose a novel physics based machine learning (PBML) model to characterize the RTSD over a discretized sub-pixelated 3D volume which is assumed. Our novel approach is the first to characterize a full 3D charge transport model of the RTSD. In this work, we first discretize the RTSD between a pixelated electrodes spatially into 3 dimensions—x, y, and z. The resulting discretizations are termed as voxels in 3D space. In each voxel, the different physics based charge transport properties such as drift, trapping, detrapping and recombination of charges are modeled as trainable model weights. The drift of the charges considers second order non-linear motion which is observed in practice with the RTSDs. Based on the electron–hole pair injections as input to the PBML model, and signals at the electrodes, free and trapped charges (electrons and holes) as outputs of the model, the PBML model determines the trainable weights by backpropagating the loss function. The trained weights of the model represents one-to-one relation to that of the actual physical charge transport properties in a voxelized detector.

Machine learning and new insights for breast cancer diagnosis.

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

Innovative flexible and compact patch antenna for multiband terahertz applications

Terahertz antennas have garnered significant attention for use in the fields of telecommunications and medical applications. One of the major issues with THz antennas is the integration of flexibility with multiband characteristics. This article focuses on designing and constructing a new, compact, and flexible dual-band THz antenna using different slots for terahertz applications. The essence of the antenna design is rooted in a careful strategy for positioning slots, both on the ground plane and within the patch. The rising need for flexible electronics is the primary motivation behind the investigation of this flexible, multiband antenna. The examined antenna is supported by a flexible polyimide substrate with small dimensions of 1.5 × 1 × 0.15 mm3. Several adjustments are made to the radiating patch to enable multiband functionality, resulting in operation at 123 GHz and 180.4 GHz. The corresponding return losses are -35.03 dB and -38.71 dB, while the bandwidths for the two frequency bands are 3.3 % and 8.86 %, respectively. For all working bands, the VSWR of the given antenna is lower than 2, with a gain of 4.12 dB and 5.05 dB at the resonating frequency, as well as a radiation efficiency of 81.6 % and 89.3 % inside the resonating bands. Moreover, a parametric investigation was additionally carried out to ascertain the ideal dimensions of the suggested antenna. As the antenna is constructed using a flexible substrate, this article also delves into the analysis of antenna bending in both the x- and y-axes. The suggested antenna can be applied in modern handheld fifth-generation (5 G) and terahertz medical imaging applications, such as breast cancer detection.

Deep Learning in Medical Imaging: A Case Study on Lung Tissue Classification

INTRODUCTION: In the field of medical imaging, accurate categorization of lung tissue is essential for timely diagnosis and management of lung-related conditions, including cancer. Deep Learning (DL) methodologies have revolutionized this domain, promising improved precision and effectiveness in diagnosing ailments based on image analysis. This research delves into the application of DL models for classifying lung tissue, particularly focusing on histopathological imagery.
 OBJECTIVES: The primary objective of this study is to explore the deployment of DL models for the classification of lung tissue, emphasizing histopathological images. The research aims to assess the performance of various DL models in accurately distinguishing between different classes of lung tissue, including benign tissue, lung adenocarcinoma, and lung squamous cell carcinoma.
 METHODS: A dataset comprising 9,000 histopathological images of lung tissue was utilized, sourced from HIPAA compliant and validated sources. The dataset underwent augmentation to ensure diversity and robustness. The images were categorized into three distinct classes and balanced before being split into training, validation, and testing sets. Six DL models - DenseNet201, EfficientNetB7, EfficientNetB5, Vgg19, Vgg16, and Alexnet - were trained and evaluated on this dataset. Performance assessment was conducted based on precision, recall, F1-score for each class, and overall accuracy.
 RESULTS: The results revealed varying performance levels among the DL models, with EfficientNetB5 achieving perfect scores across all metrics. This highlights the capability of DL in improving the accuracy of lung tissue classification, which holds promise for enhancing diagnosis and treatment outcomes in lung-related conditions.
 CONCLUSION: This research significantly contributes to understanding the effective utilization of DL models in medical imaging, particularly for lung tissue classification. It emphasizes the critical role of a diverse and balanced dataset in developing robust and accurate models. The insights gained from this study lay the groundwork for further exploration into refining DL methodologies for medical imaging applications, with a focus on improving diagnostic accuracy and ultimately, patient outcomes.

Convolutional Autoencoder-Based medical image compression using a novel annotated medical X-ray imaging dataset

With the ongoing development of machine learning techniques, it is now necessary to train and evaluate these algorithms to have access to high-quality medical X-ray datasets. This study unfolds on two critical axes within the realm of medical imaging. We introduce the proposed Medical X-ray Imaging Dataset (MXID), a meticulously curated resource featuring images spanning 18 body parts. It has been refined to include a classification based on body type within each gender category. This dataset addresses the limitations of existing datasets by offering comprehensive coverage, precise annotations, and optimal image quality. While numerous datasets often fall short in terms of encompassing a broad range of anatomical regions, particularly those that are designed for multi-body component analysis. Our primary contribution lies in bridging the gap in available resources, providing a foundation of developing robust machine learning algorithms in medical imaging. Moreover, we extend our investigation to the integration of the MXID dataset into the domain of medical image compression, evaluating the performance of techniques such as Principal Component Analysis (PCA), K-means clustering, Convolutional Neural Network (CNN), Deep Convolutional Autoencoders (DCAEs), Autoencoders (AEs), and Variational Autoencoder (VAE), using two scenarios, one is for single image compression, and the other is for MXID dataset, which offer a unique perspective that enables a comprehensive assessment of these techniques across diverse anatomical regions. Our study contributes to advancing machine learning and deep learning applications in medical imaging, emphasizing the creation of a dataset and its integration into image compression.

DeepMedSegment A Deep Learning Approach for Medical Image Segmentation

Deep Med Segment introduces an innovative approach to medical image segmentation, utilizing deeplearning techniques. Its goal is to precisely identify regions of interest within medical images, which is crucial forclinical diagnosis and treatment planning. Unlike traditional methods that rely on manual feature engineering, DeepMed Segment learns directly from data, enhancing accuracy and adaptability. Deep Med Segment employs deepconvolutional neural networks (CNNs), tailored for the complexities of medical images. It can handle variousmodalities like MRI, CT, X-ray, and ultrasound, making it versatile across medical specialties. Training requiresannotated datasets, enabling the model to map images to segmentation masks through supervised learning. To ensurerobustness, Deep Med Segment utilizes data augmentation techniques during training, enhancing its ability togeneralize across different imaging conditions. Evaluation on diverse datasets demonstrates superior performancecompared to traditional methods, with metrics like Dice similarity coefficient used for accuracy assessment. Inexperiments, Deep Med Segment consistently outperforms existing techniques, promising significant advancements inmedical imaging analysis. Its accuracy, efficiency, and adaptability make it a valuable tool for clinical diagnosis andresearch, with potential to improve patient care and healthcare outcomes. In the realm of medical image analysis,DeepMedSegment emerges as a pioneering approach harnessing the power of deep learning for image segmentation.Segmentation of medical images plays a pivotal role in clinical diagnosis, treatment planning, and monitoring ofvarious diseases. DeepMedSegment aims to tackle this challenge by leveraging advanced deep learning techniques toaccurately delineate regions of interest within medical images.One of the key strengths of DeepMedSegment lies in its ability to adapt and generalize across different modalities andimaging techniques, including magnetic resonance imaging (MRI), computed tomography (CT), X-ray, ultrasound, andmore. This versatility makes DeepMedSegment a valuable tool for a wide range of medical imaging applications,spanning from neuroimaging and oncology to cardiology and musculoskeletal imaging.

A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis

Timely identification of lung nodules, which are precursors to lung cancer, and their evaluation can significantly reduce the incidence rate. Computed Tomography (CT) is the primary technique used for lung cancer screening due to its high resolution. Identifying white, spherical shadows as lung nodules in CT images is essential for accurately detecting lung cancer. Convolutional Neural Network (CNN)-based methods have performed better than traditional techniques in various medical image applications. However, challenges still need to be addressed due to insufficient annotated datasets, significant intra-class variations, and substantial inter-class similarities, which hinder their practical use. Manually labeling the position of nodules on CT slices is critical for distinguishing between benign and malignant cases, but it is an unreliable and time-consuming process. Insufficient data and class imbalance are the primary factors that may result in overfitting and below-par performance. The paper presents a novel Deep Learning (DL) framework to detect and classify lung cancer in input CT images. It introduces a 3D-VNet architecture for accurate segmentation of pulmonary nodules and a 3D-ResNet architecture designed for their classification. The segmentation model achieves a Dice Similarity Coefficient (DSC) of 99.34% on the LUNA16 dataset while reducing false positives to 0.4%. The classification model shows performance metrics with accuracy, sensitivity, and specificity of 99.2%, 98.8%, and 99.6%, respectively. The 3D-VNet network outperforms previous segmentation methods by accurately calibrating lung nodules of various sizes and shapes with excellent robustness. The classification model’s metrics show that the suggested method outperforms current approaches regarding accuracy, specificity, sensitivity and F1-Score.

APbI3-A2AgBiI6 Double-Layer Perovskite Film for a Self-Powered and High-Stability X-ray Detector.

The development of lead halide perovskite X-ray detectors has promising applications in medical imaging and security inspection but is hindered by poor long-term stability and drift of the dark current and photocurrent. Herein, we design a (Cs0.05MA0.65FA0.3)PbI3-(Cs0.1MA1.3FA0.6)AgBiI6 double-layer perovskite film to assemble a self-powered flat-panel X-ray detector. The demonstrated X-ray detector achieves an outstanding self-powered sensitivity of 80 μC Gyair-1 cm-2 under a 0 V bias. More importantly, owing to the inhibition of the phase transition process and ion migration of (Cs0.05MA0.65FA0.3)PbI3 by the (Cs0.1MA1.3FA0.6)AgBiI6 layer, the device exhibits excellent continuous operating stability with a retention rate of 99% dark current and photocurrent over X-ray pulses of up to 4000 s and excellent long-term stability without a loss of the original response current after 150 days in an air environment. The strategy of double-layer perovskites improves the stability and sensitivity of devices, which paves a path for the industrial application of lead halide perovskite X-ray detectors.

Multi-path residual attention network for cancer diagnosis robust to a small number of training data of microscopic hyperspectral pathological images

Duct cancer is a malignant disease with higher mortality rates in males than in females, emphasizing the need for early diagnosis to improve treatment outcomes. Although various imaging modalities such as magnetic resonance imaging (MRI) and computed tomography scan (CT-scan) have been used for pathological analysis, hyperspectral imaging stands out as a promising approach, especially when combined with deep learning techniques. Hyperspectral imaging provides detailed information on tissue composition and biochemical properties, enabling better distinction between cancerous and healthy tissues. Although previous research based on hyperspectral imaging shows high accuracy, no previous research has used a small amount of training data, despite this being the usual case in medical image applications. Therefore, we propose a multi-path residual attention network (MRA-Net) with chunked residual channel attention (CRCA), which is a novel deep learning model specifically designed to address the challenges posed by limited training data, with a particular focus on using hyperspectral images. By leveraging the unique spectral information provided by hyperspectral imaging, MRA-Net extracts distinctive features, enhancing its ability to differentiate between cancerous and healthy tissues.We conducted the training and validation of our model using a publicly accessible dataset, resulting in an accuracy of 84.31% and a weighted harmonic mean of precision and recall (F1 score) of 84.29%, demonstrating its state-of-the-art performance compared to existing methods.

MEFF – A model ensemble feature fusion approach for tackling adversarial attacks in medical imaging

Adversarial attacks pose a significant threat to deep learning models, specifically medical images, as they can mislead models into making inaccurate predictions by introducing subtle distortions to the input data that are often imperceptible to humans. Although adversarial training is a common technique used to mitigate these attacks on medical images, it lacks the flexibility to address new attack methods and effectively improve feature representation. This paper introduces a novel Model Ensemble Feature Fusion (MEFF) designed to combat adversarial attacks in medical image applications. The proposed model employs feature fusion by combining features extracted from different DL models and then trains Machine Learning classifiers using the fused features. It uses a concatenation method to merge the extracted features, forming a more comprehensive representation and enhancing the model's ability to classify classes accurately. Our experimental study has performed a comprehensive evaluation of MEFF, considering several challenging scenarios, including 2D and 3D images, greyscale and colour images, binary classification, and multi-label classification. The reported results demonstrate the robustness of using MEFF against different types of adversarial attacks across six distinct medical image applications. A key advantage of MEFF is its capability to incorporate a wide range of adversarial attacks without the need to train from scratch. Therefore, it contributes to developing a more diverse and robust defence strategy. More importantly, by leveraging feature fusion and ensemble modelling, MEFF enhances the resilience of DL models in the face of adversarial attacks, paving the way for improved robustness and reliability in medical image analysis.

Deep learning methods and corresponding applications in medical imaging

Deep learning methods and corresponding applications in medical imaging

Vivaldi Tapered Slot Antenna for Microwave Imaging in Medical Applications

Microwave imaging has become an active research area in recent years, owing primarily to advancements in detecting the early stages of cancer. The study aimed to create a high-gain compact Vivaldi Tapered Slot antenna (VTSA) for microwave imaging in medical applications and also aims to address several challenges in the development of microwave imaging (MWI) technology for medical applications. These challenges include the ability to detect and identify abnormalities in human tissue and considering safe Specific Absorption Rate (SAR) limits for patients, the approach of balancing of penetration and resolution can be done on the design. The antenna operates at frequencies ranging from 1.7 to 3.1 GHz and is built on a low-cost Flame Retardant-4 (FR-4) substrate with a thickness of 1.6 mm. A compact exponential VTSA is initially presented while designing the proposed antenna for broad impedance bandwidth performances. The simulation used a back-to-back linear array of antennas with or without a phantom, specifically a without phantom (only antennas), a water phantom (cube shape), and an anomaly inside the water phantom. The results revealed a significant shift in the signal graph between the three results, indicating a difference in values between the three simulations. A transient domain solver calculation was used in the simulation. The designed antenna improved a gain of 6.09 dBi and a SAR of 0.326 W/kg by maximizing the edges of the exponential in the tapered section and the feedline slot area. The antenna exhibits differences in scattering parameters on each simulation of anomalies across the required frequency range. The result finds suitability of the experiment and simulation in assessing the microwave imaging capabilities. With the data presented, simulated antennas can be used for microwave imaging. The next study should aim on making a suitable imaging system with dimensions that supported in the antenna range and specifications.

Harnessing generative AI: Transformative applications in medical imaging and beyond

Generative AI is an expanding domain that employs machine learning models to generate novel data that closely mimic pre existing data. ChatGPT and DALL-E can be customized for specific applications and are expected to transform healthcare, education, and communication. Generative Adversarial Networks (GANs) that can generate synthetic medical images closely mimicking actual patient data may substantially enhance machine learning model training datasets. They also translate medical images from one modality to another, improve medical imaging resolution, reduce radiation exposure, and boost image quality and detail. Despite their challenges, GANs have great potential in the field of medical imaging. The key obstacles are the need for Graphic Processing Units (GPUs) and computing resources to train GANs and the lack of established standards for generating synthetic images. Incorrectly labeled data for training other machine learning models can reduce performance, making ground-truth data labeling for healthcare AI more difficult. Generative AI is revolutionizing healthcare imaging, simplifying diagnosis, and propelling healthcare research and practice to new frontiers. Ensuring the reliability and safety of generated images in medical applications requires addressing ethical considerations and validating data.