Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose significant challenges during the treatment process. Due to the importance of the issue, timely diagnosis of this illness can not only assist patients and their families in managing the condition but also enable early intervention, which may help prevent its advancement.

COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.

Background: The medical profession is facing an excessive workload, which has led to the development of various Computer-Aided Diagnosis (CAD) systems as well as Mobile-Aid Diagnosis (MAD) systems. These technologies enhance the speed and accuracy of diagnoses, particularly in areas with limited resources or remote regions during the pandemic. The primary purpose of this research is to predict and diagnose COVID-19 infection from chest X-ray images by developing a mobile-friendly deep learning framework, which has the potential for deployment in portable devices such as mobile or tablet, especially in situations where the workload of radiology specialists may be high.

Reconstructed State Space Features for Classification of ECG Signals.

Background: Cardiac arrhythmias are considered as one of the most serious health conditions; therefore, accurate and quick diagnosis of these conditions is highly paramount for the electrocardiogram (ECG) signals. Moreover, are rather difficult for the cardiologists to diagnose with unaided eyes due to a close similarity of these signals in the time domain.

Objective: In this paper, an image-based and machine learning method were presented in order to investigate the differences between the three cardiac arrhythmias of VF, VT, SVT and the normal signal.

Segmentation of brain tissues using a 3-D multi-layer hidden Markov model.

To compensate for bias field inhomogeneity and reduce noise, we incorporate domain-based knowledge and spatial information into a brain segmentation algorithm by proposing a new multi-layer Hidden Markov model. Brain tissues include Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). A typical slice of a brain image either contains GM, GM-WM or GM-WM-CSF.