has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients' data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.

Early detection of brain tumors (BTs) can save valuable lives. BTs classification is usually accomplished by using magnetic resonance imaging (MRI), which is commonly carried out earlier than definitive talent surgery. Machine learning (ML) strategies can assist radiologists to diagnose tumors barring invasive measures. One of the challenges of traditional classifiers is that they rely on informative hand-crafted features, which can be a time-consuming process to extract. We proposed fully automatic framework for BTs classification with weighted contrast-enhanced MRI images. The proposed framework includes an enhancement preprocessing to improve input images quality and a classification phase for images classification into three classes of tumors (meningioma, glioma and pituitary tumor) and ordinary cases. The model was built used "Lightweight Convolutional Neural Network (LWCNN)" that allows to automatically extract features. We tested the LWCNN model in two experiments. In the first one, the model has been tested with original datasets. We tested our proposed framework on the same dataset after enhancing the features of MRI images in the second experiment. As per the experiment results, it has been observed that the proposed framework achieves the desired outcome which demonstrates the effectiveness of our proposed framework.

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A male infant born out of non-consanguineous marriage to a primigravida presented to us as his third hospitalisation with ichthyotic lesions all over the body, cholestatic jaundice, multiple joint contractures and a history of recurrent sepsis. Blood and urine investigations revealed Fanconi syndrome, hypothyroidism and direct hyperbilirubinaemia with elevated liver enzymes and normal gamma glutamyl transpeptidase levels. The combination of arthrogryposis, renal dysfunction and cholestasis led to the suspicion of arthrogryposis, renal tubular dysfunction, cholestasis (ARC) syndrome, which was then proved by genetic testing. The baby was managed conservatively with respiratory support, antibiotics, multivitamins, levothyroxine and other supportive measures but succumbed to the illness on day 15 of hospitalisation. Genetic analysis using next-generation sequencing was confirmatory of a homozygous mutation in VIPAS39 gene leading to ARC syndrome type 2 in the present case. Genetic counselling was provided and prenatal testing was advised to the parents for future pregnancies.