Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems, 2nd Edition

Dear Colleagues,

Machine and deep learning algorithms have recently seen broad use in computer-assisted diagnostic systems due to their dramatic advances in image analysis, computer vision, and time-series analysis. Deep and machine learning has demonstrated their huge potential to transform computer-aided diagnosis in a wide variety of areas that range from medical disease diagnostics and classification, through mechanical systems condition monitoring, to diagnosis for chemical industries, as well as structural health diagnosis of different structures as bridges, wind turbines, or buildings.

This Special Issue calls for innovative work that explores recent advances, prospects, and challenges in artificial intelligence applications to reduce the chances of either missing, misclassifying, or overdiagnosing suspicious targets on diagnostic, as well as to propel the path into computer-assisted prognostics. It is noteworthy that in this Special Issue the keyword ‘diagnostic’ has to be understood in a wide sense: medical, mechanical systems, civil engineering, chemical processes, and so on. The targeted audience includes both academic researchers and industrial practitioners. The purpose is to provide a platform to enhance interdisciplinary research and collaborations and to share the most innovative ideas in various related fields.

Prof. Dr. José Rodellar
Dr. Francesc Pozo
Dr. Yolanda Vidal
Guest Editors

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• deep learning
• machine learning
• artificial intelligence
• fault diagnosis
• damage diagnosis
• disease diagnosis
• medical decision making
• real-time diagnostics