Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF detection from dynamic ECGs. In this study, we explored

Electric contact currents (CC) can cause muscle contractions, burns, or ventricular fibrillation which may result in life-threatening situations. In vivo studies with CC are rare due to potentially hazardous effects for participants. Cadaver studies are limited to the range of tissue’s electrical properties and the utilized probes’ size, relative position, and sensitivity. Thus, the general safety

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance

Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and

Medical confidentiality is an essential duty in the medical profession. In this paper, we introduce a novel robust blind watermarking method for securing the medical report in a DICOM (Digital Imaging and Communications in Medicine) imaging. The insertion of each bit is done in the subtraction of adjacent DC coefficients of the FFT (fast Fourier transformation) using a key k w . Each bit of the watermark

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost