Improved state refinement for LSTM determined 3D CAISR-LSTM model for automatic myocardial infarction detection.

Electrocardiograms (ECGs) contain valuable information in the clinical diagnosis of myocardial infarction (MI). However, its interpretation process is dependent on cardiologists with extensive clinical experience and expertise. The issue not only causes a paucity of medical resources, but also restricts patients from receiving timely diagnoses.

Stochastic-based learning for image classification in chest X-ray diagnosis.

Objectives: The current research introduces a stochastic deep learning method with the aim of enhancing lung disease detection, particularly pneumonia, in chest X-ray images. The goal is to improve diagnostic precision and help facilitate more effective clinical practices.

Methods: We deployed a better convolutional neural network architecture, thoroughly optimized with dropout regularization and aggressive data augmentation to support classification performance as well as model resilience.