Classification in Clinical Applications covers the most current advances in applying classification techniques to a wide variety of clinical applications, appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management, and computer aided diagnosis (CAD) systems design. Classification techniques are used for medical image analysis as a part of computer aided diagnosis systems, which employ machine learning, data analysis, pattern recognition and other deep learning techniques. Classification is an important statistical/mathematical tool used to refine and analyze complex systems such as medical images. The clinical applications can be across a wide range of diseases/procedures/specialties and are included as examples in the book. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN), and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images because they contain tools for data pre-processing, classification, regression, clustering, association rules and visualization. Medical image classification is essential for accurate diagnosis and development of computer aided diagnosis (CAD) systems in clinical applications. For physicians, automated classification of medical images is an increasingly significant tool in their daily activity.

Advances in medical imaging technology supported by computer science have enhanced interpretation of medical images and contributed to early diagnosis. The development of CAD systems provides a method of assisting physicians in the detection of abnormalities, quantification of disease progress and alternate diagnosis of lesions. Extraction of appropriate features is a vital step in any image classification. Furthermore, since medical images are often highly textured, texture analysis becomes crucial in medical image analysis.