Medical data processing has grown into a prominent topic in the latest decades with the
primary goal of maintaining patient data via new information technologies, including the …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in
sha** community health and overall quality of life. Traditional healthcare methods, often …

Abstract Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn
new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL …

Nowadays, machine learning (ML) has attained a high level of achievement in many
contexts. Considering the significance of ML in medical and bioinformatics owing to its …

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of
identification, ECG leakage seems to be a common occurrence due to the development of …

To improve the detection of COVID-19, this paper researches and proposes an effective
swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method …

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness,
and significant propensity for metastasis. The predominant categories of cancer that may …

Historical patients' medical data has an important impact on the healthcare industry for
providing the best care to patients through intelligent health diagnosis and prediction of …