Computed Tomography Scan Imaging Research Articles

Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds.

A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.

Fine_Denseiganet: Automatic Medical Image Classification in Chest CT Scan Using Hybrid Deep Learning Framework

Medical image classification is one of the most significant tasks in computer-aided diagnosis. In the era of modern healthcare, the progress of digitalized medical images has led to a crucial role in analyzing medical image analysis. Recently, accurate disease recognition from medical Computed Tomography (CT) images remains a challenging scenario which is important in rendering effective treatment to patients. The infectious COVID-19 disease is highly contagious and leads to a rapid increase in infected individuals. Some drawbacks noticed with RT-PCR kits are high false negative rate (FNR) and a shortage in the number of test kits. Hence, a Chest CT scan is introduced instead of RT-PCR which plays an important role in diagnosing and screening COVID-19 infections. However, manual examination of CT scans performed by radiologists can be time-consuming, and a manual review of each individual CT image may not be feasible in emergencies. Therefore, there is a need to perform automated COVID-19 detection with the advances in AI-based models. This work presents effective and automatic Deep Learning (DL)-based COVID-19 detection using Chest CT images. Initially, the data is gathered and pre-processed through Spatial Weighted Bilateral Filter (SWBF) to eradicate unwanted distortions. The extraction of deep features is processed using Fine_Dense Convolutional Network (Fine_DenseNet). For classification, the Softmax layer of Fine_DenseNet is replaced using Improved Generative Adversarial Network_Artificial Hummingbird (IGAN_AHb) model in order to train the data on the labeled and unlabeled dataset. The loss in the network model is optimized using Artificial Hummingbird (AHb) optimization algorithm. Here, the proposed DL model (Fine_DenseIGANet) is used to perform automated multi-class classification of COVID-19 using CT scan images and attained a superior classification accuracy of 95.73% over other DL models.

Abnormal distribution of fat tissue and its association with intestinal failure-associated liver disease in children and adolescents with long-time parenteral nutrition support: A case-control study.

Patients with intestinal failure (IF) often present with abnormal body composition characterized by high fat mass. However, the distribution of fat and its association with the development of IF-associated liver disease (IFALD) remain unclear. This study aims to investigate the body composition and its relationship with IFALD in older children and adolescents with IF. This retrospective case-control study enrolled patients with IF receiving parenteral nutrition (PN) at Keio University Hospital who initiated PN before the age of 20 years (cases). The control group included patients with abdominal pain, with available computed tomography (CT) scan and anthropometric data. CT scan images of the third lumbar vertebra (L3) were used for body composition analysis and compared between the groups. Liver histology was compared with CT scan findings in IF patients who underwent biopsy. Nineteen IF patients and 124 control patients were included. To account for age distribution, 51 control patients were selected. The median skeletal muscle index was 33.9 (29.1-37.3) in the IF group and 42.1 (39.1-45.7) in the control group (P < 0.01). The median visceral adipose tissue index (VATI) was 9.6 (4.9-21.0) in the IF group and 4.6 (3.0-8.3) in the control group (P = 0.018). Among the 13 patients with IF who underwent liver biopsies, 11 (84.6%) had steatosis, and there was a tendency for fibrosis to correlate with VATI. Patients with IF exhibit low skeletal muscle mass and high visceral fat, which may be related to liver fibrosis. Routine monitoring of body composition is recommended.

Plane invariant segmentation of computed tomography images through weighted cross entropy optimized conditional GANs in compressed formats.

Computed tomography (CT) scan provides first-hand knowledge to doctors to identify an ailment. Deep neural networks help enhance image understanding through segmentation and labeling. In this work, we implement two variants of Pix2Pix generative adversarial networks (GANs) with varying complexities of generator and discriminator networks for plane invariant segmentation of CT scan images and subsequently propose an effective generative adversarial network with a suitably weighted binary cross-entropy loss function followed by image processing layer necessary for getting high-quality output segmentation. Our conditional GAN is powered by a unique set of an encoder-decoder network that coupled with the image processing layer produces enhanced segmentation. The network can be extended to the complete set of Hounsfield units and can also be implemented on smartphones. Furthermore, we also demonstrate effects on accuracy, F-1 score, and Jaccard index by using the conditional GAN networks on the spine vertebrae dataset, thus achieving an average of 86.28 % accuracy, 90.5 % Jaccard index score, and 89.9 % F-1 score in predicting segmented maps for validation input images. In addition, an overall lifting of accuracy, F-1 score, and Jaccard index graph for validation images with better continuity has also been highlighted.

Comparative Analysis of Hounsfield Units and Vertebral Bone Quality Scores for Predicting Proximal Junctional Failure in Female Adult Spinal Deformity Patients Undergoing Planned 2-Stage Corrective Surgery with Lateral Lumbar Interbody Fusion

This study aimed to evaluate the utility of computed tomography (CT)-based Hounsfield units (HUs) and magnetic resonance imaging-based Vertebral Bone Quality (VBQ) scores as alternatives to dual-energy x-ray absorptiometry for predicting the risk of proximal junctional failure (PJF) in female patients with adult spinal deformity (ASD) undergoing 2-stage corrective surgery with lateral lumbar interbody fusion (LLIF). The study included 53 female patients with ASD who underwent 2-stage corrective surgery with LLIF from January 2016 to April 2022 with a minimum follow-up of 1year. CT and magnetic resonance imaging scans were evaluated for their correlation with PJF. Of the 53 patients (mean age 70.2years), 14 had PJF. Patients with PJF had significantly lower HU values at the upper instrumented vertebra (UIV) (113.0±29.4 vs. 141.1±41.5, P=0.036) and L4 (113.4±59.5 vs. 160.0±64.9, P=0.026) than those without PJF. However, there was no difference in VBQ scores between the 2 groups. PJF correlated with HU values at UIV and L4 but not with VBQ scores. Patients with PJF also had significantly different pre- and postoperative thoracic kyphosis, postoperative pelvic tilt, pelvic incidence minus lumbar lordosis, and proximal junctional angle compared to those without PJF. The findings suggest that measuring HU values at UIV or L4 by CT may be useful for predicting the risk of PJF in female ASD patients undergoing 2-stage corrective surgery with LLIF. Therefore, CT-based HUs should be considered in ASD surgery planning to reduce the risk of PJF.

TCCD Fusion Imaging to Estimate Intracranial Pressure and Tissue Displacement with Large Hemispheric Infarction.

Despite breakthroughs in stroke treatment, some patients still experience large infarctions of the cerebral hemispheres resulting in mass effect and tissue displacement. The evolution of mass effect is currently monitored using serial computed tomography (CT) imaging. However, there are patients who are ineligible for transport, and there are limited options for bedside monitoring of unilateral tissue shift. We used fusion imaging for overlaying transcranial color duplex with CT angiography. This method allows overlay of live ultrasound on top of CT or magnetic resonance imaging scans. Patients with large hemispheric infarctions were eligible to participate. Position data from the source files were used and matched with live imaging and correlation to magnetic probes on the patient's forehead and ultrasound probe. Shift of cerebral parenchyma, displacement of the anterior cerebral arteries, basilary artery and third ventricle were analyzed, as well as pressure on the midbrain, and the displacement of the basilar artery on the head were analyzed. Patients received multiple examinations in addition to standard care of treatment with CT imaging. The sensitivity for diagnosing a shift of 3mm with fusion imaging was 100%, with a specificity of 95%. No side effects or interactions with critical care equipment were recorded. Fusion imaging is an easy method to access and acquire measurements for critical care patients and follow-up of tissue and vascular displacement after stroke. Fusion imaging may be a decisive support for indicating hemicraniectomy.

Fahr’s Syndrome Secondary to Hypoparathyroidism Presenting with Paralysis and Recurrent Seizures: A Case Report

Aim: To describe a rare case of Fahr’s syndrome (FS) associated with chronic post-surgical hypoparathyroidism and hypocalcemia.&#x0D; Case Presentation: A 63-year-old female with a previous history of total thyroidectomy and hemiplegia presented to our hospital with altered mentation and recurrent generalized tonic-clonic seizures. Laboratory evaluation revealed hypoparathyroidism, hypocalcemia, and hypokalemia. Head computed tomography (CT) scan was consistent with FS, demonstrating extensive, bilateral, and symmetrical calcified deposits in the brain, predominantly in basal ganglia, corona radiata, and cerebellar hemispheres. The association with post-surgical hypoparathyroidism and relevant biochemical indices determined the suspected etiology of the pathologic process of ectopic calcification.&#x0D; Discussion: FS is a rare, neurodegenerative disorder characterized by abnormal bilateral ectopic calcified deposits in the basal ganglia and other brain structures. FS presents with a wide variety of neurological and psychiatric manifestations. The diagnosis is confirmed by neuroimaging studies such as a head CT scan or magnetic resonance imaging, which displays the calcification of BG and other structures in a bilateral and symmetrical pattern. Biochemical analysis may adjunctively identify the underlying risk factor of the disease.&#x0D; Conclusion: Our case represents a long-term severe consequence of untreated post-surgical hypoparathyroidism, which has consequently led to irreversible secondary FS. Maintenance of eucalcemic and euphosphatemic states is essential to prevent the progression of ectopic cerebral calcification.

HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using Computed Tomography Images and Text

Prosthetic joint infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients’ numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based Feature Fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4% and an area under the curve (AUC) of 95.9%, outperforming recent multimodal approaches by 2.9% in ACC and 2.2% in AUC, with a parameter count of only 68 M. Notably, the interpretability results highlighted our model’s strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.

Anterior Cervical Discectomy and Fusion Performed Using a CaO-SiO2-P2O5-B2O3 Bioactive Glass Ceramic or Polyetheretherketone Cage Filled with Hydroxyapatite/β-Tricalcium Phosphate: A Prospective Randomized Controlled Trial.

A CaO-SiO2-P2O5-B2O3 bioactive glass-ceramic (BGS-7) spacer provides high mechanical stability, produces a chemical bond to the adjacent endplate, and facilitates fusion after spine surgery. This prospective, randomized, single-blind, non-inferiority trial aimed to evaluate the radiographic outcomes and clinical efficacy of anterior cervical discectomy and fusion (ACDF) using a BGS-7 spacer for treating cervical degenerative disorders. Thirty-six patients underwent ACDF using a BGS-7 spacer (Group N), and 40 patients underwent ACDF using polyetheretherketone (PEEK) cages filled with a mixture of hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP) for the treatment of cervical degenerative disorders. The spinal fusion rate was assessed 12 months postoperatively using three-dimensional computed tomography (CT) and dynamic radiographs. Clinical outcomes included patient-reported outcome measures, visual analog scale scores for neck and arm pain, and scores from the neck disability index (NDI), European Quality of Life-5 Dimensions (EQ-5D), and 12-item Short Form Survey (SF-12v2). All participants were randomly assigned to undergo ACDF using either a BGS-7 spacer or PEEK cage filled with HA and β-TCP. The primary outcome was the fusion rate on CT scan image at 12 months after ACDF surgery based on a per-protocol strategy. Clinical outcomes and adverse events were also assessed. The 12-month fusion rates for the BGS-7 and PEEK groups based on CT scans were 81.8% and 74.4%, respectively, while those based on dynamic radiographs were 78.1% and 73.7%, respectively, with no significant difference between the groups. There were no significant differences in the clinical outcomes between the two groups. Neck pain, arm pain, NDI, EQ-5D, and SF-12v2 scores significantly improved postoperatively, with no significant differences between the groups. No adverse events were observed in either group. In ACDF surgery, the BGS-7 spacer showed similar fusion rates and clinical outcomes as PEEK cages filled with HA and β-TCP.

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

Incidence et typologie des fractures concomitantes du capitellum associées aux fractures du col et de la tête radiale

BackgroundRadial head and neck fracture is a common fracture around the elbow. It is mostly caused by axial or valgus loading after a fall in elbow extension and forearm pronation. When a radial head and neck fracture occurs, the capitellum collides with the radial head and experiences a shear force, leading to capitellum fracture. The primary aim of this study was to evaluate the incidence of concurrent capitellum fracture and analyze its patterns among patients treated for radial head and neck fractures based on computed tomography. Secondary aim is to determine factors associated with concurrent capitellum fractures. HypothesisCapitellum fractures are more common than expected in radial head and neck fractures and show a consistent pattern. With the higher the Mason type fracture, the higher the incidence of concurrent capitellum fracture will be shown. MethodsA total of 101 patients treated surgically (27 patients) or non-surgically (74 patients) for radial head and neck fractures from January 2017 to December 2020 were retrospectively analyzed. Initial computed tomography was reviewed to identify concurrent capitellum fractures. A fracture was diagnosed when the bone continuity was lost in two consecutive images in any one of the three directions of computed tomography scan images. Seventeen patients with capitellum fractures were classified as group 1 and 84 patients without capitellum fractures as group 2. Each group of patients was assessed using Mason classification, whether fracture extended to the radial neck or not, fracture location, and treatment method. To determine the fracture location, radial head was trisected using three anatomical landmarks: safe zone, posteromedial zone, and anteromedial zone. And it was determined as the location where the fracture was most comminuted. ResultsThe average age of the 101 patients was 44.6 years old. There were 54 (53.5%) males and 47 (46.5%) females. Capitellum fracture was accompanied in 17 cases. Its incidence was 16.8%. The rate of accompanying capitellum fracture was 9.5% (6/63) for Mason type 1, 25.0% (6/24) for Mason type 2, and 41.7% (5/12) for Mason type 3. The ratio of radial neck extension of fracture was significantly higher in group 2. There was no isolated radial neck fracture in group 1. However, there were 33 in group 2. After eliminating isolated radial neck fracture patients, significantly more fractures were located in the safety zone in group 1. Group 1 had a significantly higher rate of surgical treatment than group 2 (52.9% (9/17) vs. 21.4% (18/84) p = 0.007). ConclusionFor radial head and neck fractures, the higher the Mason type fracture, the higher the incidence of concurrent capitellum fracture. When the fracture extended to the radial neck, the comorbidity of the capitellum fracture was decreased. Among radial head fractures, when the fracture was located in the safe zone, the comorbidity of capitellum fracture was increased. The rate of surgical treatment was higher in radial head and neck fracture patients with concurrent capitellum fractures. Level of evidenceIV; diagnostic study.

Analysis of reconstructed multisource and multiscale 3-D digital rocks based on the cycle-consistent generative adversarial network method

SUMMARY Digital rock physics (DRP) is important for characterizing the pore characteristics and petrophysical parameters of rocks from a microscopic perspective. Among the digital rock reconstruction methods, the most widely used is the computer tomography (CT) scan method. However, the pore structure of rocks has multiscale features, and CT scan images with a single resolution cannot completely describe the pore structure characteristics of rocks. In this paper, we analysed reconstructed multisource and multiscale 3-D digital rocks based on the cycle-consistent generative adversarial network (CycleGAN) method. This method fully integrates the advantages of the large field of view of low-resolution images and the high-precision features of high-resolution images. To investigate the performance of the method, two sequences of CT scan images of a sandstone (sample A) and a carbonate rock (sample B) collected from oilfields were studied. Moreover, due to the high resolution of scanning electron microscope (SEM) images, we also constructed 3-D digital rocks with different resolutions of the Berea sandstone based on the combination of CT scan images and SEM images. Finally, the statistical properties and absolute permeabilities were calculated to evaluate the accuracies of reconstructed multisource and multiscale 3-D digital rocks. The results show that the reconstructed multiscale digital rocks based on the CycleGAN method have good accuracy in terms of statistical properties and petrophysical properties. Considering the computational cost and computational accuracy, for high- and low-resolution CT scan images and CT scan images with SEM images as training image data sources, we suggest that the resolution of the reconstructed multiscale digital cores is 4–8 times and 4–16 times higher than that of the low-resolution CT scan images, respectively. The findings of our research will be helpful in gaining insight into the petrophysical properties of heterogeneous rocks.

Novel Architecture for Image Classification Based on Rough Set

The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.

CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images.

The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body's internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).

Positioning and design by computed tomography imaging in neuroendoscopic surgery of patients with chronic subdural hematoma

Neuroendoscopy is a very useful technique to Chronic Subdural Hematoma (CSH). But how to achieve the goal of treatment more minimally invasive? To develop a simple, fast and accurate preoperative planning method in our way for endoscopic surgery of patients with CSH. From June 2018 to May 2020, forty-two patients with CSH, admitted to our hospital, were performed endoscopic minimally invasive surgery; computed tomography (CT) imaging was employed to locate the intracerebral hematoma and select the appropriate endoscopic approach before the endoscopic surgery. The clinical data and treatment efficacy were analyzed. According to the learning of CT scanning images, the surgeon can accurately design the best minimally invasive neuroendoscopic surgical approach and realize the precise positioning and design of the drilling site of the skull and the size of the bone window, so as to provide the most effective operation space with the smallest bone window. In this group, the average operation time was only about 1 h, and the clearance rate of hematoma was about 95%. Patients with CSH can achieve good therapeutic effect by using our way to positioning and design to assist the operation of CSH according to CT scan and image, and our way is very useful and necessary.

Liver fat analysis using optimized support vector machine with support vector regression.

Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people. An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR). The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage. The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics. The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN).

COVID‐19: A systematic review of prediction and classification techniques

AbstractCOVID‐19 has affected more than 760 million people all over the world, as per the latest record of the WHO. The rapid proliferation of COVID‐19 patients not only created a health emergency but also led to an economic crisis. An early and accurate diagnosis of COVID‐19 can help in combating this deadly virus. In line with this, researchers have proposed several machine learning (ML) and deep learning (DL) techniques for detecting COVID‐19 since 2020. This article presents currently available manual diagnosis methods along with their limitations. It also provides an extensive survey of ML and DL techniques that can support medical professionals in the precise diagnosis of COVID‐19. ML methods, namely K‐nearest neighbor, support vector machine (SVM), artificial neural network, decision tree, naive bayes, and DL methods, viz. deep neural network, convolutional neural network (CNN), region‐based convolutional neural network, and long short‐term memories, are explored. It also provides details of the latest COVID‐19 open‐source datasets, consisting of x‐ray and computed tomography scan images. A comparative analysis of ML and DL techniques developed for COVID‐19 detection in terms of methodology, datasets, sample size, type of classification, performance, and limitations is also done. It has been found that SVM is the most frequently used ML technique, while CNN is the most commonly used DL technique for COVID‐19 detection. The challenges of an existing dataset have been identified, including size and quality of datasets, lack of labeled datasets, severity level, data imbalance, and privacy concerns. It is recommended that there is a need to establish a benchmark dataset that overcomes these challenges to enhance the effectiveness of ML and DL techniques. Further, hurdles in implementing ML and DL techniques in real‐time clinical settings have also been highlighted. In addition, the motivation noticed from the existing methods has been considered for extending the research with an optimized DL model, which attained improved performance using statistical and deep features. The optimized deep model performs better than 90% based on efficient features and proper classifier tuning.

Classification of COVID-19 Patient Based on Multilayer Perceptron Neural Networks Optimized with Garra Rufa Fish Optimization Using CT Scan Images

COVID-19 is known in recent times as a severe syndrome of respiratory organ (Lungs) and has gradually produced pneumonia, a lung disorder all around the world. As coronavirus is continually spreading rapidly globally, the computed tomography (CT) technique has been made important and essential for quick diagnosis of this dangerous syndrome. Hence, it is necessitated to develop a precise computer-based technique for assisting medical clinicians in identifying the COVID-19 influenced patients with the help of CT scan images. Therefore, the multilayer perceptron neural networks optimized with Garra Rufa Fish optimization using images of CT scan is proposed in this paper for the classification of COVID-19 patients (COV-19-MPNN-GRF-CTI). The input images are taken from SARS-COV-2 CT-scan dataset. Initially, the input images are pre-processed utilizing convolutional auto-encoder (CAE) to enhance the quality of the input images by eliminating noises. The pre-processed images are fed to Residual Network (ResNet-50) for extracting the global and statistical features. The extraction over the features of CT scan images is made through ResNet-50 and subsequently input to multilayer perceptron neural networks (MPNN) for CT images classification as COVID-19 and Non-COVID-19 patients. Here, the layer of Batch Normalization of the MPNN is separated and added with ResNet-50 layer. Generally, MPNN classifier does not divulge any adoption of optimization approach for calculating the optimal parameters and accurately classifying the extracted features of CT images. The Garra Rufa Fish (GRF) optimization algorithm performs to optimize the weight parameters of MPNN classifiers. The proposed approach is executed in MATLAB. The performance metrics, such as sensitivity, precision, specificity, [Formula: see text]-measure, accuracy and error rate, are examined. Then the performance of the proposed COV-19-MPNN-GRF-CTI method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, 33.98%, 21.95%, 34.78% lower error rate compared with the existing methods, like multi-task deep learning using CT image analysis for COVID-19 pneumonia classification and segmentation (COV-19-MDP-CTI), COVID-19 classification utilizing CT scan depending on meta-classifier approach (COV-19-SEMC-CTI) and deep learning-based COVID-19 prediction utilizing CT scan images (COV-19-CNN-CTI), respectively.

Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective

Owing to the profoundly irresistible nature of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, an enormous number of individuals halt in the line for Computed Tomography (CT) scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of a threshold-based image segmentation technique and random forest classifier for COVID-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify COVID-19 individuals into three asperity classes such as early, progressive, and advanced, with an accuracy of 95.5% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify coronavirus severity.