Medical X-ray Imaging Research Articles

Single activator doped fluoride nanoparticles for X-ray excited high-resolution and delayed flexible nonplanar imaging

Lanthanide doped fluoride nanoparticles (NPs) exhibit adjustable X-ray excited optical/persistent luminescence (XEOL/XEPL) and possessing extensive potential in XEPL-based delay imaging. However, the high-resolution and delayed XEPL-based delay imaging always requires strict introduction of sensitized or heterovalent ions and the construction of complex structures for excellent XEPL performance of lanthanide-doped fluoride NPs, which is attributed to a lack of effective and convenient strategies. Here we show that the intense XEPL strength was obtained by single Tb3+ activated ion doping and simple CsY2F7@NaYF4 core/shell structure for X-ray excited high-resolution and delayed flexible nonplanar imaging. The strict introduction and control of sensitized or heterovalent ions, and the construction of intricate multilayer nanostructure are avoided. In addition, we enable the NPs to achieve high and long-term XEPL performance by simply thermal processing strategy. We successfully mixed minimized amount of the NPs into flexible film to create a scintillation screen which consequently exhibit high surface smoothness, environmental stability, and transparency. Our results demonstrate high resolution delayed imaging of the NPs integrated flexible film for the interior of nonplanar objects. The spatial resolution of 14.3 lp mm−1 is substantially higher than the typical spatial resolutions required for medical CT scans (2 lp mm−1) and X-ray imaging (3.5 lp mm−1). These findings will promote the development of advanced delayed X-ray nonplanar imaging applications.

Millimeter-scale soft capsules for sampling liquids in fluid-filled confined spaces.

Sampling liquids in small and confined spaces to retrieve chemicals and microbiomes could enable minimally invasive monitoring human physiological conditions for understanding disease development and allowing early screening. However, existing tools are either invasive or too large for sampling liquids in tortuous and narrow spaces. Here we report a fundamental liquid sampling mechanism that enables millimeter-scale soft capsules for sampling liquids in confined spaces. The miniature capsule is enabled by flexible magnetic valves and superabsorbent polymer, fully wirelessly controlled for on-demand fluid sampling. A group of miniature capsules could navigate in fluid-filled and confined spaces safely using a rolling locomotion. The integration of on-demand triggering, sampling, and sealing mechanism and the agile group locomotion allows us to demonstrate precise control of the soft capsules, navigating and sampling body fluids in a phantom and animal organ ex vivo, guided by ultrasound and x-ray medical imaging. The proposed mechanism and wirelessly controlled devices spur the next-generation technologies for minimally invasive disease diagnosis.

Radiation shielding calculation for interventional radiology: An updated workload survey using a dose monitoring software

Shielding design is an essential aspect of radiation protection. It is necessary to ensure that barriers safeguard workers, patients, the general public, and the environment from the harmful radiation emitted by X-ray machines. The National Council on Radiation Protection and Measurements (NCRP) 147 method is widely accepted within the radiation protection experts’ (RPEs) community for structural shielding design for medical X-ray imaging facilities. However, these indications are based on data collected in 1996. In recent years, interventional radiology procedures have seen significant developments. Therefore, it is important to evaluate whether updating the data on workload in the different specialities is necessary.We extracted all interventional radiology exposure data parameters from three angiographs from two vendors using dose monitoring software for 3066 procedures and 214,697 individual exposures. The workload distribution as a function of the kVp for five interventional rooms was calculated by summing all exposures and then normalising them by the number of patients.Analysing this data, we obtained new transmission curves through lead, concrete and gypsum wallboard, finding the parameters (α, β, and γ) in the Archer equation for the secondary radiation. Finally, our aim was to share an example of shielding calculations for haemodynamics and neuroangiography rooms to illustrate the impact of updated transmission data.

SSP: self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: a broad experimental study

Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence of joint replacement surgery, it can be anticipated that joint replacement revision surgery will become more common. It can be challenging at times to retrieve the manufacturer of the in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) can help correctly identify the implanted prosthesis. Correct identification of implants in revision surgery can help reduce perioperative complications and complications. DL was used in this study to categorise different implants based on X-ray images into four classes (as a first case study of the small dataset): Cofield, Depuy, Tornier, and Zimmer. Imbalanced and small public datasets for shoulder implants can lead to poor performance of DL model training. Most of the methods in the literature have adopted the idea of transfer learning (TL) from ImageNet models. This type of TL has been proven ineffective due to some concerns regarding the contrast between features learnt from natural images (ImageNet: colour images) and shoulder implants in X-ray images (greyscale images). To address that, a new TL approach (self-supervised pertaining (SSP)) is proposed to resolve the issue of small datasets. The SSP approach is based on training the DL models (ImageNet models) on a large number of unlabelled greyscale medical images in the domain to update the features. The models are then trained on a small labelled data set of X-ray images of shoulder implants. The SSP shows excellent results in five ImageNet models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, and EfficientNet with precision of 96.69%, 95.45%, 98.76%, 98.35%, and 96.6%, respectively. Furthermore, it has been shown that different domains of TL (such as ImageNet) do not significantly affect the performance of shoulder implants in X-ray images. A lightweight model trained from scratch achieves 96.6% accuracy, which is similar to using standard ImageNet models. The features extracted by the DL models are used to train several ML classifiers that show outstanding performance by obtaining an accuracy of 99.20% with Xception+SVM. Finally, extended experimentation has been carried out to elucidate our approach’s real effectiveness in dealing with different medical imaging scenarios. Specifically, five different datasets are trained and tested with and without the proposed SSP, including the shoulder X-ray with an accuracy of 99.47% and CT brain stroke with an accuracy of 98.60%.

Scatter estimation and correction using time-of-flight and deconvolution in x-ray medical imaging.

Objective Time-of-flight (TOF) scatter rejection requires a total timing jitter,
including the detector timing jitter and the X-ray source's pulses width, of 50 ps or less
to mitigate most of the effects of scattered photons in radiography and CT imaging.
However, since the total contribution of the source and detector to the timing jitter
can be retrieved during an acquisition with nothing between the source and detector,
it can be demonstrated that this contribution may be partially removed to improve
the image quality.
Approach A scatter correction method using iterative deconvolution of the measured
time point-spread function estimates the number of scattered photons detected in each
pixel. To evaluate the quality of the estimation, GATE was used to simulate the
radiography of a water cylinder with bone inserts, and a head and torso in a system
with total timing jitters from 100 ps up to 500 ps full-width-at-half-maximum.
Main results With a total timing jitter of 200 ps, 89% of the contrast degradation
caused by scattered photons was recovered in a head and torso radiography, compared
to 28% with a simple time threshold method. Corrected images using the estimation
have a percent root-mean square error between 2 and 14% in both phantoms with
timing jitters from 100 to 500 ps which is lower than the error achieved with scatter
rejection alone at 100 ps.
Significance TOF X-ray imaging has the potential to mitigate the effects of the
scattering contribution and offers an alternative to anti-scatter grids that avoids loss
of primary photons. Compare to simple TOF scatter rejection using only a threshold,
the deconvolution estimation approach has lower requirements on both the source and
detector. These requirements are now within reach of state-of-the-art systems.

Medical X-ray images enhancement based on super resolution convolution neural network

<p>Pneumonia is a severe lung infection, chest X-ray (CXR) image preferred to find infection. Real images lost its quality, resolution and other feature due to transmission. So good qualitative datasets are very limited. Quality enhancement in medical images is challenging task for researchers. And quality in clinical diagnosis of any disease in deep learning play a very important role. So, this paper presents an aspect with importance of quality in medical images CXR of a particular dataset and how to enhance and create new images with high quality resolution, that is re-used for classification in deep learning. Super resolution convolutional neural netwok (SRCNN) is deep learning based method, which is used for improving resolution in image. Super resolution means low resolution (LR) images from dataset is to be reconstructed or magnified into high resolution (HR). The objective behind this study is to measure the effect of super resolution with quality index, peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). This experinment performed on 200 images with 10 batches, each batch has 20 images from Kermany dataset, select LR images and converted into HR with SRCNN. Then we find PSNR value of image is increase upto 2 to 5 DB, and MSE of qood quality images is near to zero and MSE decrease up to 20-25, SSIM value have little variation due to same pattern is found in input and output images. Enhancement means highlight or improve the region of interest of pneumonic images. Main goal of this study is to preapare a modified dataset which is further used for classification.</p>

Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.

Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval. We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model. Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset. The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.

Characterizing the response of Timepix solid state detectors to dust impacts

Timepix Solid State Detectors (TSSDs) are used in a range of applications, including high energy particle physics, x-ray imaging spectroscopy, medical imaging, radiation monitors and compact dosimeters. In space sciences, such detectors have been used for characterizing the radiation environments of the Earth and lunar surface. This study is a pilot investigation of the response of TSSDs to high-velocity dust impacts. The setup used for the measurements is a silicon slab with 500μm thickness combined with the Timepix 2 read-out chip with a pixel size of 55×55 μm2. The outer surface of the SSD is coated with a 150 nm thick layer of aluminum to eliminate the sensitivity to light. The dust accelerator facility operated at the University of Colorado is used to expose the TSSD to micron- and sub-micron sized iron particles in a velocity range of 1–10 km/s. A fraction (5%) of the impacting dust particles were detected by the TSSD. The primary mechanism of the detection is through the light generated upon the impact and the charge signal spreads over multiple pixels. The fraction of dust particles kinetic energy that is converted to detectable charge on the TSSD is small, in the range of 10−3−10−6. The largest craters generated by the dust impacts were examined using a scanning electron microscope (SEM). The SEM images confirm that the particles penetrated the aluminum layer and the craters contain iron residue from the dust material. Over the investigated impact parameter range, the TSSD does not suffer a permanent damage from the dust impacts. Due to their small size and relatively low sensitivity to dust impacts, TSSDs could be used as dust detectors only in environments with high dust fluxes (e.g., in cometary trails), or where the precision measurements of the impact location are desired.

Mn(II)-Based Metal Halide with Near-Unity Quantum Yield for White LEDs and High-Resolution X-ray Imaging.

Metal halides have drawn great interest as luminescent materials and scintillators due to their outstanding optical properties. Exploring new types of phosphors with easy production processes, excellent photophysical properties, high light yields, and environmentally friendly compositions is crucial and quite challenging. Herein, a novel Mn(II)-based metal halide (4-BTP)2MnBr4 was produced using a facile solvent evaporation method, which exhibited a strong green emission peaking at 524 nm from the d-d transition of tetrahedral-coordinated Mn2+ ion and a near-unity quantum yield. The prepared white light-emitting diode device has a wide color gamut of 100.7% NTSC with CIE chromaticity coordinates of (0.32, 0.32). In addition, (4-BTP)2MnBr4 demonstrates excellent characteristics in X-ray scintillation, including a high light yield of 98 000 photons/MeV, a sensitive detection limit of 37.4 nGy/s, excellent resistance to radiation damage, and successful demonstration of X-ray imaging with high resolution at 21.3 lp/mm, revealing the potential for application in diagnostic X-ray medical imaging and industry radiation detection.

Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and wiener filtering approach

In the evolving landscape of medical diagnostics, a paradigm shift is being catalysed by the advent of generative artificial intelligence. Medical X-ray, CT and MRI images are essential diagnostic tools used by healthcare professionals to assess various musculoskeletal conditions. However, obtaining a sufficient number of medical images for training deep learning models can be challenging due to limited access to labelled data. Hence, the authors propose a novel Latent diffusion process for synthesizing medical images that closely resemble real patient images, aiming to address the challenge of limited access to labelled data in medical diagnostics. Leveraging deep learning and generative modelling techniques, this method synthesizes high-fidelity images that closely mimic real patient scans. By introducing noise and subsequently training the model to denoise, the approach captures intricate patterns inherent in authentic medical images. Among the four datasets utilized, the Diabetes Retinopathy dataset demonstrates superior performance, achieving the highest Mean Structural Similarity Index (MSSIM) score of 0.57 (compared to the dataset baseline of 0.62) and an accuracy of 93.75% when passed through the proposed pipeline. The Cataract dataset, registered a MSSIM score of 0.51 (versus the dataset baseline of 0.53) and an accuracy score of 97.52%, while the Knee OA dataset follows closely with MSSIM and accuracy scores of 0.65 (in contrast to the dataset baseline of 0.63) and 68.66% respectively. The results obtained are then compared with the results generated by the other state of the art models.

Cross and local optimal avoidance of RIME algorithm: A segmentation study for COVID-19 X-ray images

Image segmentation is a crucial technique in analyzing X-ray medical images as it aids in uncovering relevant information concealed within a patient's body, a pivotal aspect of the diagnostic process. The effectiveness of computer-aided diagnosis systems largely depends on the accuracy of the image processing methods. In recent years, multi-threshold image segmentation methods have found widespread application in medical image analysis. Despite the effectiveness of some renowned methods for binary threshold segmentation, the field still faces challenges due to the high cost of threshold computation. Metaheuristic algorithms hold the potential to address this issue as they can produce sufficiently reasonable solutions with manageable computational overheads. While some similar methods have been proposed, the imbalance between exploration and exploitation results in instability as the number of thresholds increases. Consequently, these solutions suffer from reduced efficiency in computing thresholds. In this study, a variant of the latest RIME algorithm, termed SLCRIME, is proposed. This paper replaces the pseudo-random method with low-discrepancy Sobol sequences for solution initialization. Additionally, two methods aimed at avoiding local optima and promoting information exchange within the solution set are introduced, further enhancing its capability to search for optimal threshold sets for IS systems. Subsequently, a multi-threshold image segmentation model based on SLCRIME is proposed and applied to segment 6 COVID-19 X-ray images. In the experiments, SLCRIME is compared with 6 peer algorithms, and the results are evaluated using image segmentation accuracy, feature similarity index metrics (FSIM), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM). The analysis indicates that SLCRIME achieves optimal thresholds at reasonable computational costs and outperforms other algorithms in terms of performance.

A combined analytical and Monte Carlo method for detailed simulations of antiscatter grids in x-ray medical imaging: implementing scatter within the grid

Objective. To implement a hybrid method, which combines analytical tracking and interaction simulation using Monte Carlo (MC) techniques, in order to model photon transport inside antiscatter grids (ASG) for x-ray imaging. Approach. A new tally was developed for PENELOPE (v.2018) and penEasy (v. 2020) MC code to simulate photon transmission through ASGs. Two established analytical algorithms from the literature were implemented in this tally. In addition, a new hybrid method was introduced by extending one of the analytical algorithms to include photon-interactions inside the grid, while preserving the imaged grid structure. Calculations of primary (TP), scatter (TS), and total (TT) grid transmissions in addition to the Q factor (Q = TP2/TT ) were performed. The new tally was validated for a quadric geometry ASG, and experimental measurements with a PMMA phantom of several thicknesses. In addition, the contribution of the scatter inside the grid was studied for three interspace materials, and a high resolution image of the grid was simulated. Main results. An excellent agreement was found between the two analytical models compared with the quadric grid without scatter, and the hybrid method with the geometrical grid with scatter. Average deviations of 0.2% and 1.4% were found between TP and TS for the hybrid method and quadric grid, while for the hybrid method and experimental measurements these values were 1% and 20%. Antiscatter grids with aluminium as interspace material had the highest amount of scatter from inside the grid to the final image, followed up by paper fibre and air. The high resolution image of the grid was equivalent using the quadric geometry or the hybrid mode. Significance. The hybrid method provides a means of studying scattered radiation from the antiscatter grid with the advantage of higher performance, with results that are consistent with a full quadric geometry simulation of the ASG.

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.

Inorganic metal oxide material BaSiO3:Eu2+ for convenient 3D X-ray imaging

X-ray storage phosphors are found wide-ranging applications in medical diagnosis, nondestructive testing and X-ray imaging. However, designing X-ray storage phosphors with high electron storage capacity still poses a daunting challenge. In this study, we designed a fluorescent powder based on Eu2+-doped BaSiO3, demonstrating excellent optical information storage characteristic. Under high-energy X-ray filling, shallow traps exhibit low electron capacity and only produce weak afterglow, which greatly helps to minimize the impact of afterglow on the image quality during X-ray imaging. In addition, the deep traps at 1.01 eV filled with high-energy X-ray allow the material to store information for a long time. The crystal structure, photoluminescence (PL) and thermoluminescence (TL) spectra were studied systematically. The electron trapping properties of the material were analyzed using TL spectroscopy. Finally, we fabricated a flexible film with phosphors, the flexible film has been utilized to demonstrate high-quality real-time X-ray imaging and achieve time-delayed X-ray imaging of curved objects, such as flexible circuit boards.

Role of Quantum Dots and Nanostructures in Photovoltaic Energy Conversion

Nanostructures and quantum dots have substantial effects on enhancing photovoltaic energy conversion efficiency, as evidenced in this comprehensive study. Materials that are nanostructured and nanosized particles are commonly used to address the urgent issues related to energy conversion. The use of nanostructured substances to address issues with energy and natural resources has garnered a lot of interest lately. Directional nanostructures in particular show promise for the conversion, collection, and storage of energy. Due to their unique properties, such as electrical conductivity, mechanical energy, and photoluminescence, quantum dots made from carbon (CQDs) and graphene quantum dots (GQDs) have been integrated into hybrid photovoltaic-thermoelectric systems (PV-TE). It evaluates the effects of nanostructures on solar energy technologies, in particular how they can improve power conversion and light absorption in solar cells. Optical light detectors, which transform photonic energy into signals that are electrical, are among the many optoelectronic uses of CQDs that have drawn attention because they are essential components of contemporary imaging and communication systems, such as visible light cameras, machine vision, medical X-ray and near-infrared image processing, and visible light detection devices. Besides supercapacitors, the study investigates how nanostructures could play a crucial role in contributing to addressing the global energy crisis sustainably, by working as photocatalysts for hydrogen synthesis and supercapacitors.

Design of Barnacle Mating Optimizer with Deep Learning Based Classification Model for Medical X-Ray Images

Design of Barnacle Mating Optimizer with Deep Learning Based Classification Model for Medical X-Ray Images

COVID-19 Diagnosis Applied DWT and CNN on X-ray Chest Images

Background: Medical images have many important applications, and this importance increased when the emergence of the COVID-19 pandemic. These applications have been focused on computed tomography chest images and X-ray images. This research will focus on special X-ray medical image applications of coronavirus (COVID-19).Methods: Many methods are applied on medical images to achieve certain features. The designed approach is implemented through many steps starting from preprocessing up to classification step. The proposed approach focusing on generating efficient features using discrete wavelet transform (DWT) then applying convolutional neural network (CNN) to classify between normal and abnormal COVID-19.Results: The COVID-19 diagnosis approach is implemented to achieve high performance system. The obtained result of COVID-19 diagnosis applied CNN tool leading to validation accuracy of 92.31%.Conclusion: Hybridizing two technologies (DWT and CNN) is intended to reach the best results in the diagnostic process. In addition, X-ray chest image is an important tool for detection and diagnosis of COVID-19 diseases.

Fractional-order modified heterogeneous comprehensive learning particle swarm optimizer for intelligent disease detection in IoMT environment

This paper presents an alternative early disease detection technique based on the Internet of Medical Things (IoMT) to improve the healthcare system. Since it is necessary to address different aspects to help healthcare organizations to improve their efficiency. Early disease detection using a fast and efficient test, such as chest X-ray images, is one of the most important issues. In this paper, we develop an efficient model to allocate abnormalities in medical chest X-ray images. The developed model consists of a set of stages; the first stage is to segment the images using a multilevel thresholding technique to determine the lung inside the image, then extracting the features from the segmented objects using different extractor methods. Then, we proposed a Fractional-order modified Heterogeneous comprehensive learning particle swarm optimizer (FMHCLPSO) as a feature selection method to determine the relevant features used to improve the detection process. To evaluate the performance of the developed IoMT model, a set of three medical datasets is used, called Covid-19 & Pneumonia, X-raycovid, and Radiography. The results illustrate the high efficiency of the developed model to detect diseases based on performance measures. Furthermore, We compared the proposed method to several existing methods, and it showed significant performance.

Effective Brain Stroke Prediction with Deep Learning Model by Incorporating YOLO_5 and SSD

Ischemic stroke is a life-threatening disorder that significantly reduces a person’s lifespan. The timely diagnosis of stroke heavily relies on medical imaging techniques such as magnetic resonance imaging (MRI), computerized tomography (CT), and x-ray imaging. However, the manual localization and analysis of these images can be time-consuming and yield less accurate results. To address this challenge, we propose the implementation of deep-learning object detection techniques for computerized lesion identification in medical images. In this study, we employ three categories of deep learning object identification networks: deep convolutional neural network (DCNN), you only look once (YOLO) 5, and single-shot detector (SSD). By leveraging these advanced deep learning models, we aim to reduce the effort and time required for screening and analyzing a significant number of daily medical images, including MRI, CT, and x-ray images. With the addition of YOLO5 and SSD among these networks, the accuracy achieved was 96.43%, demonstrating their effectiveness in accurately identifying lesions associated with ischemic stroke.

Detection of Post COVID-Pneumonia Using Histogram Equalization, CLAHE Deep Learning Techniques

Pneumonia, also known as bronchitis, is caused by bacteria, viruses, or fungi. Pneumonia can be fatal to an infected person because the lungs cannot exchange air. The disease primarily affects infants and people over the age of 65. Every year, nearly 4 million people are killed by the disease, which affects an estimated 420 million people. It is critical to detect and diagnose this condition as soon as possible. Diagnosing the condition using the patient's x-ray is the most effective method. Experienced radiologists will use a chest x-ray of the affected patient to make this informed decision. Recently, coronavirus is a contagious viral disease caused by the SARSCoV2 virus. This virus affects the human respiratory system. The virus also causes pneumonia (COVID pneumonia), which is far more dangerous than normal pneumonia. The main purpose of this task is to study and compare several deep learning enhancement techniques applied to medical x-ray and CT scan images for the detection of COVID19 (pneumonia).
 A convolutional neural network (CNN) is used to design a model that can distinguish between COVID19 pneumonia and normal pneumonia. In addition, image enhancement techniques (histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE)) have been processed against the dataset to find more efficient methods and models for detecting pneumonia. A dataset of 6432 CXRs were used - 576 COVID pneumonia CXRs, 1583 normal pneumonia CXRs, and 4273 healthy lung CXRs. Based on the results, it was observed that the equalized histogram and the equalized dataset of CLAHE run faster than the original dataset. This requires a computer-aided diagnosis (CAD) system that can distinguish between COVID pneumonia, normal pneumonia, and healthy lungs. In addition, the improved VGG16 achieved 96% accuracy in the detection of X-ray images of COVID19 - pneumonia.