Enhancement of a Biomedical Instrument using Machine Learning

Automated detection in medical diagnostics such as imaging has become an emergent field. There is always a risk of exposing patients to issues brought on by human error in all areas of medicine. Among the most promising Machine Learning (ML) applications in the medical field is the analysis of chest X-rays (CXR). Yet, because of the complex structure of radiographs, the accurate identification and classification of specific diseases in CXR collections is still a challenging problem. A study reported that the level of the range of inaccuracy in radiology differs based on the medical examinations and is between 2 and 30%. The likelihood of errors also increases during the night shift hours, whereas a lower chance during the day shift hours. These mistakes may have devastating consequences for falsely diagnosed patients. This underlines the necessity for automated abnormality identification in x-rays, which might reduce the possibility of mistakes and produce reliable findings. This research study intends to enhance a medical instrument with good accuracy and specificity by assessing the results of different ML techniques.