The classification of wheat yellow rust disease based on a combination of textural and deep features.

Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models.

Diagnosing and differentiating viral pneumonia and COVID-19 using X-ray images.

Coronavirus-caused diseases are common worldwide and might worsen both human health and the world economy. Most people may instantly encounter coronavirus in their life and may result in pneumonia. Nowadays, the world is fighting against the new coronavirus: COVID-19. The rate of increase is high, and the world got caught the disease unprepared. In most regions of the world, COVID-19 test is not possible due to the absence of the diagnostic kit, even if the kit exists, its false-negative (giving a negative result for a person infected with COVID-19) rate is high. Also, early detection of COVID-19 is crucial to keep its morbidity and mortality rates low. The symptoms of pneumonia are alike, and COVID-19 is no exception. The chest X-ray is the main reference in diagnosing pneumonia. Thus, the need for radiologists has been increased considerably not only to detect COVID-19 but also to identify other abnormalities it caused. Herein, a transfer learning-based multi-class convolutional neural network model was proposed for the automatic detection of pneumonia and also for differentiating non-COVID-19 pneumonia and COVID-19. The model that inputs chest X-ray images is capable of extracting radiographic patterns on chest X-ray images to turn into valuable information and monitor structural differences in the lungs caused by the diseases. The model was developed by two public datasets: Cohen dataset and Kermany dataset. The model achieves an average training accuracy of 0.9886, an average training recall of 0.9829, and an average training precision of 0.9837. Moreover, the average training false-positive and false-negative rates are 0.0085 and 0.0171, respectively. Conversely, the model's test set metrics such as average accuracy, average recall, and average precision are 97.78%, 96.67%, and 96.67%, respectively. According to the simulation results, the proposed model is promising, can quickly and accurately classify chest images, and helps doctors as the second reader in their final decision.

End-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays.

Lumbar spondylolisthesis (LS) is the anterior shift of one of the lower vertebrae about the subjacent vertebrae. There are several symptoms to define LS, and these symptoms are not detected in the early stages of LS. This leads to disease progress further without being identified. Thus, advanced treatment mechanisms are required to implement for diagnosing LS, which is crucial in terms of early diagnosis, rehabilitation, and treatment planning. Herein, a transfer learning-based CNN model is developed that uses only lumbar X-rays. The model was trained with 1922 images, and 187 images were used for validation. Later, the model was tested with 598 images. During training, the model extracts the region of interests (ROIs) via Yolov3, and then the ROIs are split into training and validation sets. Later, the ROIs are fed into the fine-tuned MobileNet CNN to accomplish the training. However, during testing, the images enter the model, and then they are classified as spondylolisthesis or normal. The end-to-end transfer learning-based CNN model reached the test accuracy of 99%, whereas the test sensitivity was 98% and the test specificity 99%. The performance results are encouraging and state that the model can be used in outpatient clinics where any experts are not present.

3-State Protein Secondary Structure Prediction based on SCOPe Classes

Abstract Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q3 accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.

The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis.

Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network.

INTRODUCTION: Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis. METHODS: A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA. RESULTS: The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500. CONCLUSION: Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.

Some Ethical Issues in Prehospital Emergency Medicine.

Prehospital emergency medical care has many challenges including unpredictable patient profiles, emergency conditions, and administration of care in a non-medical area. Many conflicts occur in a prehospital setting that require ethical decisions to be made. An overview of the some of ethical issues in prehospital emergency care settings is given in this article. Ethical aspects of prehospital emergency medicine are classified into four groups: the process before medical interventions, including justice, stigmatization, dangerous situations, and safe driving; the treatment process, including triage, refusal of treatment or transport, and informed consent; the end of life and care, including life-sustaining treatments, prehospital cardiopulmonary resuscitation (CPR), withholding or withdrawal of CPR, and family presence during resuscitation; and some ambulance perception issues, including ambulance misuse, care of minors, and telling of bad news. Prehospital emergency medicine is quite different from emergency medicine in hospitals, and all patients and situations are unique. Consequently, there are no quick formulas for the right action and emotion. It is important to recognize the ethical conflicts that occur in prehospital emergency medicine and then act to provide the appropriate care that is of optimal value.

Attitudes of prehospital emergency care professionals toward refusal of treatment: A regional survey in Turkey.

INTRODUCTION: Prehospital emergency medicine is a specific field of emergency medicine. The basic approach of prehospital emergency medicine is to provide patients with medical intervention at the scene of the incident. This special environment causes health professionals to encounter various problems. One of the most important problems in this field is ethics, in particular questions involving refusal of treatment and the processes associated with it. OBJECTIVE: The objective of this study is to identify emergency health professionals' views regarding refusal of treatment. METHODS: This study was conducted with 356 health professionals who were on active duty in prehospital emergency health services. The data were collected through a form which included 10 statements. The participants were asked to indicate their level of agreement with the statements given by rating them between 0 and 10. ETHICAL CONSIDERATIONS: Before conducting the research, permission was received from the local ethics committee. Participants were given written information about the purpose of the study. Participants were assured that their participation was voluntary. RESULTS: The healthcare professionals with fewer years of experience in the profession and female participants adopted an attitude of giving priority to providing care. Young participants, in general, respected patient autonomy. However, paradoxically, when it comes to emergency medical cases, they expressed an opinion closer to paternalism. CONCLUSIONS: This study has found that prehospital emergency health professionals generally respect the patient's right to refuse treatment; however, they do not prioritize this right when there is a life-threatening situation or when the person does not have decision-making capacity. In these cases, prehospital emergency health professionals tended to adopt a more paternalistic approach.

A case study from the perspective of medical ethics: refusal of treatment in an ambulance.

This paper will examine a sample case encountered by ambulance staff in the context of the basic principles of medical ethics. An accident takes place on an intercity highway. Ambulance staff pick up the injured driver and medical intervention is initiated. The driver suffers from a severe stomach ache, which is also affecting his back. Evaluating the patient, the ambulance doctor suspects that he might be experiencing internal bleeding. For this reason, venous access, in the doctor's opinion, should be achieved and the patient should be quickly started on an intravenous serum. The patient, however, who has so far kept his silence, objects to the administration of the serum. The day this is taking place is within the month of Ramadan and the patient is fasting. The patient states that he is fasting and that his fast will be broken and his religious practice disrupted in the event that the serum is administered. The ambulance doctor informs him that his condition is life-threatening and that the serum must be administered immediately. The patient now takes a more vehement stand. 'If I am to die, I want to die while I am fasting. Today is Friday and I have always wanted to die on such a holy day,' he says. The ambulance physician has little time to decide. How should the patient be treated? Which type of behaviour will create the least erosion of his values?