The phrase "abnormality detection" is correct and usable in written English.
It can be used in contexts related to identifying unusual patterns or anomalies in data, systems, or behaviors.
Example: "The software employs advanced algorithms for abnormality detection to ensure system integrity."
Alternatives: "anomaly detection" or "irregularity identification".
Novelty detection is an interesting research topic in many data analytics and machine learning tasks ranging from video security surveillance, network abnormality detection, and detection of abnormal gene expression sequence to name a few.
ACR/BIRADS classification will be beneficial, to characterize what the mammogram density is since it is a parameter criterion in Breast Imaging Reporting and Data System classification, as well as to create an optimal approach to follow if, for example, detecting masses in mammographic image of tissue abnormality detection (marcocalcification/microcalcification).
Image segmentation plays a vital role in MRI abnormality detection.
Second, a robust supervised abnormality detection strategy is proposed.
2: Each -locator sends a Loc_ack message to the sensor, including the message abnormality detection result.
Abnormality detection is one of the most explored applications that involves analysis of trajectories.
The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink.
Figure 4 EEG-based automated abnormalities detection and classification scheme: (a) Block diagram of traditional single analysis.
These methods described above can be assessed in terms of performance against quality criteria for EEG abnormalities detection and classification.
The performance of T-F features in EEG abnormalities detection and localization (Figure 4- b)) is now assessed using real multichannel EEG signals recorded from newborns.
Tables 3 and 4 show the applicability of these T-F image processing techniques to the EEG abnormalities detection and classification problem.
