Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.

The use of computer-aided diagnosis in the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. This paper explores the effect of various popular image enhancement techniques and states the effect of each of them on the detection performance. We have compiled the largest X-ray dataset called COVQU-20, consisting of 18,479 normal, non-COVID lung opacity and COVID-19 CXR images. To the best of our knowledge, this is the largest public COVID positive database. Ground glass opacity is the common symptom reported in COVID-19 pneumonia patients and so a mixture of 3616 COVID-19, 6012 non-COVID lung opacity, and 8851 normal chest X-ray images were used to create this dataset. Five different image enhancement techniques: histogram equalization, contrast limited adaptive histogram equalization, image complement, gamma correction, and Balance Contrast Enhancement Technique were used to improve COVID-19 detection accuracy. Six different Convolutional Neural Networks (CNNs) were investigated in this study. Gamma correction technique outperforms other enhancement techniques in detecting COVID-19 from standard and segmented lung CXR images. The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96.29%, 96.28%, 96.29%, 96.28% and 96.27% respectively. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11 %, 94.55 %, 94.56 %, 94.53 % and 95.59 % respectively for segmented lung images. The proposed approach with very high and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images

Spleen injuries are among the most frequent trauma-related injuries. At present, they are classified according to the anatomy of the injury. The optimal treatment strategy, however, should keep into consideration the hemodynamic status, the anatomic derangement, and the associated injuries. The management of splenic trauma patients aims to restore the homeostasis and the normal physiopathology especially considering the modern tools for bleeding management. Thus, the management of splenic trauma should be ultimately multidisciplinary and based on the physiology of the patient, the anatomy of the injury, and the associated lesions. Lastly, as the management of adults and children must be different, children should always be treated in dedicated pediatric trauma centers. In fact, the vast majority of pediatric patients with blunt splenic trauma can be managed non-operatively. This paper presents the World Society of Emergency Surgery (WSES) classification of splenic trauma and the management guidelines.

https://link.springer.com/content/pdf/10.1186/s13017-017-0151-4.pdf

Splenic trauma: WSES classification and guidelines for adult and pediatric patients

COVID-19 appears to be having a devastating influence on world health and wellbeing. Moreover, the COVID-19 confirmed cases have recently increased to over 10 million worldwide. As the number of verified cases increase, it is more important to monitor and classify healthy and infected people in a timely and accurate manner. Many existing detection methods have failed to detect viral patterns. Henceforth, by using COVID-19 thoracic x-rays and the histogram-oriented gradients (HOG) feature extraction methodology; this research work has created an accurate classification method for performing a reliable detection of COVID-19 viral patterns. Further, the proposed classification model provides good results by leveraging accurate classification of COVID-19 disease based on the medical images. Besides, the performance of our proposed CNN classification method for medical imaging has been assessed based on different edge-based neural networks. Whenever there is an increasing number of a class in the training network, the accuracy of tertiary classification with CNN will be decreasing. Moreover, the analysis of 10 fold cross-validation with confusion metrics can also take place in our research work to detect various diseases caused due to lung infection such as Pneumonia corona viruspositive or negative. The proposed CNN model has been trained and tested with a public X-ray dataset, which is recently published for tertiary and normal classification purposes. For the instance transfer learning, the proposed model has achieved 85% accuracy of tertiary classification that includes normal, COVID-19 positive and Pneumonia. The proposed algorithm obtains good classification accuracy during binary classification procedure integrated with the transfer learning method. Journal of ISMAC (2021) Vol.03/ No.02 Pages: 132-148 http://irojournals.com/iroismac/ DOI: https://doi.org/10.36548/jismac.2021.2.006

https://irojournals.com/iroismac/V3/I2/06.pdf

Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

/pdf/artificial-intelligence-based-classification-of-chest-x-ray-1q6upikro0.pdf

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

/pdf/covid-19-deep-learning-prediction-model-using-publicly-4zzi2zr79k.pdf

COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

The spleen is one of the organs most frequently injured in blunt abdominal trauma. Computed tomography (CT) scanning can accurately detect splenic injury and is currently the imaging modality of choice in assessing clinically stable patients with blunt abdominal trauma. The CT features of spleen injury include lacerations, subcapsular or parenchymal haematomas, active haemorrhage, and vascular injuries. We present a pictorial review of the spectrum of CT findings for blunt splenic injuries. This article will be a useful reference for radiologists and surgeons as CT scan is widely used for the assessment of splenic injuries and contributes to the current trend towards nonsurgical management of this injury.

Computed tomography of blunt spleen injury: a pictorial review.

Dysphagia of vascular origin is termed dysphagia lusoria and it is relatively uncommon. Amongst the vascular causes, aberrant right subclavian artery is the most common. This case illustrates the usefulness of imaging in the investigation of dysphagic patient for an accurate diagnosis and appropriate management of the condition.

Dysphagia lusoria caused by an aberrant right subclavian artery

Blunt abdominal trauma can cause multiple internal injuries. However, these injuries are often difficult to accurately evaluate, particularly in the presence of more obvious external injuries. Computed tomography (CT) imaging is currently used to assess clinically stable patients with blunt abdominal trauma. CT can provide a rapid and accurate appraisal of the abdominal viscera, retroperitoneum and abdominal wall, as well as a limited assessment of the lower thoracic region and bony pelvis. This paper presents examples of various injuries in trauma patients depicted in abdominal CT images. We hope these images provide a resource for radiologists, surgeons and medical officers, as well as a learning tool for medical students.

/pdf/computed-tomography-ct-imaging-of-injuries-from-blunt-3rf79zd3pe.pdf

Computed tomography (CT) imaging of injuries from blunt abdominal trauma: a pictorial essay

Retrocaval ureter is a rare cause of hydronephrosis. Its rarity and non-specific presentation pose a challenge to surgeons and radiologists in making the correct diagnosis. Differentiation from other causes of urinary tract obstruction, especially the more common urolithiasis, is important for successful surgical management. Current practice has seen multislice computed tomography (MSCT) rapidly replaces intravenous urography (IVU) in the assessment of patients with hydronephrosis due to suspected urolithiasis, especially ureterolithiasis. However, MSCT, without adequate opacification of the entire ureter, may allow the physician to overlook a retrocaval ureter as the cause of hydronephrosis. High-resolution IVU images can demonstrate the typical appearance that leads to the accurate diagnosis of a retrocaval ureter. We reported a case that illustrates this scenario and highlights the importance of IVU in the assessment of a complex congenital disorder involving the urinary tract.

/pdf/retrocaval-ureter-the-importance-of-intravenous-urography-1o6okz1a1n.pdf

Radhiana Hassan

Papers

COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

Computed tomography of blunt spleen injury: a pictorial review.

Dysphagia lusoria caused by an aberrant right subclavian artery

Computed tomography (CT) imaging of injuries from blunt abdominal trauma: a pictorial essay

Retrocaval ureter: the importance of intravenous urography.