Chest X-Ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-Aided Diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (Frontal view) and CheXpert (Frontal and Lateral view). The results show that our proposed method outperformed current state-of-the-art.

An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high...

The medical information represents an invaluable source of knowledge concerning the medical history of the patient, but the manner of their presentation make it badly exploited. The idea of this paper is based on the analysis of the death reports written in natural language, which are rich of information, and can be exploited in the calculation of mortality statistics, giving preventive solutions, as well as, help medical professional in their research. This paper proposes our approach to the task of Multi-label Categorization of French death certificates according to ICD-10 (International Classification of Diseases) codes. This approach is based on Machine learning techniques, which is evaluated over CépiDC corpus. The experiment showed that our approach gives interesting results, with an average F1-measue of 79.02%.

With the continued growth of confirmed cases of COVID-19, a highly infectious disease caused by a newly discovered coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2, or SARS-CoV-2, there is an urgent need to find ways to help clinicians fight the virus by reducing the workload and speeding up the diagnosis of COVID-19. In this work, we propose an artificial intelligence solution “AI_COVID” which can help radiologists to know if the lungs are infected with the virus in just a few seconds. AI_COVID is based on a pre-trained DenseNet-121 model that detects subtle changes in the lungs and an SVM classifier that decides whether these changes are caused by COVID-19 or other diseases. AI_COVID is trained on thousands of frontal chest x-rays of people who have contracted COVID-19, healthy people, and people with viral or bacterial pneumonia. The experimental study is tested on 781 chest x-rays from two publicly available chest x-ray datasets COVID-19 radiography database and COVIDx Dataset. The performance results showed that our proposed model (DenseNet-121 + SVM) demonstrated high performance and yielded excellent results compared to the current methods in the literature, with a total accuracy of 99.74% and 98.85% for binary classification (COVID-19 vs. No COVID-19) and multi-class classification (COVID-19 vs. Normal vs. Pneumonia), respectively.