Machine Learning Applications for Computer-Aided Medical Diagnostics

Machine learning has made potential developments in biotechnology. Years of medical training are required for correct diagnosis of diseases. Diagnostics is often a very time-consuming process, and it requires strenuous effort. Data generated through varieties of imaging modalities for the diagnoses purpose is very bulky. In the corporate and government hospitals, a high number of patients are visiting per day for the disease diagnosis and treatment. This may cause diagnosis burden on the clinicians and radiologist. For interpretation, overload of image data may produce oversight and observational errors. Machine learning algorithms have recently made huge advancements in automated disease detection and classification. These algorithms can learn to view the patterns in an image similarly the way doctors do by training those using lots of annotated examples. Various machine learning algorithms used for automated diagnosis in medical imaging filed are discussed in the paper. Comparative analysis of these algorithms based on different parameters is also presented. This paper also focused at various applications of machine learning in diagnostic imaging, which can be part of routine clinical work for detection and classification of the process.