COVID-19 severity detection using chest X-ray segmentation and deep learning.

COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus's variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework's reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings.

Chiari III malformation in a neonate.

Chiari malformation (CM) is a group of complex deformities of the posterior fossa and hindbrain, of which CMIII is the rarest. We report a term neonate, with an antenatal diagnosis of occipital encephalocele, who underwent resection of the encephalocele and ligation of vessels, with repair of a large scalp defect and dural reconstruction on day 4 of life. The parents of the child had been counselled for a guarded and poor prognosis on initial diagnosis. The child has had a good postoperative course without complications but suffers from cortical visual impairment and global developmental delay.

An efficient real-time stock prediction exploiting incremental learning and deep learning.

Intraday trading is popular among traders due to its ability to leverage price fluctuations in a short timeframe. For traders, real-time price predictions for the next few minutes can be beneficial for making strategies. Real-time prediction is challenging due to the stock market's non-stationary, complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. Machine learning models are considered effective for stock forecasting, yet, their hyperparameters need tuning with the latest market data to incorporate the market's complexities. Usually, models are trained and tested in batches, which smooths the correction process and speeds up the learning. When making intraday stock predictions, the models should forecast for each instance in contrast to the whole batch and learn simultaneously to ensure high accuracy. In this paper, we propose a strategy based on two different learning approaches: incremental learning and Offline-Online learning, to forecast the stock price using the real-time stream of the live market. In incremental learning, the model is updated continuously upon receiving the stock's next instance from the live-stream, while in Offline-Online learning, the model is retrained after each trading session to make sure it incorporates the latest data complexities. These methods were applied to univariate time-series (established from historical stock price) and multivariate time-series (considering historical stock price as well as technical indicators). Extensive experiments were performed on the eight most liquid stocks listed on the American NASDAQ and Indian NSE stock exchanges, respectively. The Offline-Online models outperformed incremental models in terms of low forecasting error.