Human fall detection is a critical area of research in the field of healthcare and assistive technologies. Accurate and timely detection of falls is crucial for ensuring the well-being and safety of individuals, particularly the elderly and those with mobility impairments.
Abnormal detection, also known as anomaly detection, plays a crucial role in various domains, including cybersecurity, industrial monitoring, and healthcare. Detecting abnormal events or behaviors that deviate from expected patterns is essential for identifying potential threats, system failures, or critical health conditions.
The preprocessing of electrocardiogram (ECG) signals is a critical step in cardiac research and clinical practice. ECG signals are prone to various types of noise and artifacts that can obscure important cardiac information and affect diagnostic accuracy.
Content-Based Image Retrieval (CBIR) is a prominent research area within the field of visual information retrieval. It focuses on developing techniques and algorithms to retrieve images from large databases based on their visual content rather than relying on textual descriptions.
The preprocessing of breast cancer data is a critical phase in biomedical research, significantly influencing the reliability and accuracy of subsequent analyses. The paper systematically explores and discusses various preprocessing techniques specifically tailored for breast cancer research.
The preprocessing of colon-related data is a crucial aspect of biomedical research, particularly in studies focused on colon cancer or other colon-related diseases. This process involves a series of steps to ensure the quality, reliability, and relevance of the data before conducting analyses.
The preprocessing of lung-related data is a pivotal phase in biomedical research, particularly in studies focused on lung diseases such as lung cancer or respiratory conditions. This multifaceted process involves various steps aimed at ensuring the integrity, accuracy, and relevance of the data before any analyses are conducted.
Retinal vessel segmentation is a critical step in medical image analysis, specifically in the domain of ophthalmology. This process involves the extraction of blood vessels from retinal images, providing valuable information for various applications, such as diagnosing diseases like diabetic retinopathy and hypertensive retinopathy.