Many ailments can be identified by using different modality signals and images such as EEG, ECG, EOG, ERG, EMG, CT, MRI, etc. Hence, there is massive influx of huge multimodality patient data to be analysed quickly and accurately. This requires a high level of scientific programming. Many machine learning (ML) algorithms have been developed to automatically detect the diseases using various feature extraction methods from the images. Extracting the proper features from the medical data using advanced signal processing methods using normal programming is a challenging task. Hence, nowadays advanced scientific programming methods such as Heath 4.0 and deep learning (DL)-based programming is widely used for automated diagnosis.

The advanced scientific programming methods including DL techniques like convolution neural networks (CNN), long short- term memory (LSTM), autoencoder, deep generative models, and deep belief networks have been applied for big data efficiently. Application of such novel scientific programming methods to medical data can aid the clinicians to make an accurate and fast diagnosis.

The aim of this Special Issue is to collate original research and review articles describing advances in this field.

Potential topics include but are not limited to the following:

• Scientific Programming and Deep learning for ECG and EEG signals, and CT and MRI images
• Advanced machine learning scientific programming for health informatics
• Scientific Programming for Health 4.0
• Deep neural networks for ERG and EOG
• Nature-based scientific programming methods for biomedical signal and image processing
• Deep learning vs traditional machine learning comparative analysis of bio-signals
• Reviews on various soft computing-based scientific programming architectures for biomedical signals
• Scientific Programming in VLSI-based Health Informatics
• Scientific Programming for medical imaging-based healthcare
• Scientific Programming for real-time analysis of health data for sports, fitness, etc.
• Scientific Programming for medical statistics analysis like ANOVA
• Online Scientific Programming for healthcare and emergency