Published Paper Details:

Artificial intelligence (AI) and Machine learning (ML), and Deep Learning (DL) have been applicable practicable due to the advancements in computing facilities. Almost every application in general in Industry 4.0 is going to use these advanced mathematical tools and techniques of AI and ML and DL. SMART Healthcare is not an exception for this. In healthcare applications a lot of imaging data is generated as far as diagnostic imaging modalities like Magnetic Resonance Imaging (MRI) are concerned. MR imaging is a very popular noninvasive diagnostic imaging modality with very good soft tissue contrast. Therefore, imaging data resulting from MR imaging is used in this research. Although, there are several other medical imaging modalities. The same MRI has several clinical applications beyond routine imaging, which include functional MR Imaging (fMRI) of brain, Cardic MR imaging, MR Spectroscopy (MR) and MR Spectroscopic Imaging (MRSI), and many more. It requires that the data be processed automatically and in real time for the modern SMART healthcare diagnostic purpose. In spite of the technological developments in Magnetic Resonance Imaging, clinical applications demand accurate, reliable and real time data processing, for the purpose of making diagnostic decisions. Internet of things (IOT) has made it possible to collect the real time data and there are modern Data Analytics methods which make it possible to derive meaningful conclusions from the medical information that is available. This is then processed for providing medical diagnostic information to the medical expert, in a suitable form. In this thesis, the applicability of AI, ML and DL methods have been proposed to be investigated as applied to MR Imaging to make it suitable for providing SMART Healthcare solutions. Not only obtaining the diagnostic information but also the usability aspects of AI, ML and DL based approaches shall be attempted in this research. MR Imaging data from diagnostic centers or Hospitals, shall be used for this purpose. The results shall be compared with the standards methods in MR imaging and also verified with the experts from medical field.