Name of United Graduate School of Agricultural Sciences:Tottori University
Assigned university:Shimane university
Specialized field:Applied Bioresource Chemistry
Research Theme:Application of Machine Learning methods to Raman hyperspectral data for robust classification and early diagnosis of diseases
Obtained (planned) degree/date:Doctor of Philosophy (Bioresource and Life Sciences) March 2024 (scheduled)


Chronic disease incidences and mortality are on a rise across the world in recent years. Early diagnosis and classification of such diseases can not only help to find a cure but also plays an important role to find a suitable drug delivery system. Raman spectroscopy can be used as a diagnostic tool which can provide the information at molecular level. It’s application in diagnosis of diseases is already proven and very well established. However, to study the Raman spectra, various Machine Learning methods are used but existing methods are not efficient and requires professional expertise but prone to human made errors. To overcome these difficulties, we can make use of recent advances in Artificial Intelligence field such as neural networks or Deep Learning which is also a sub-class of Machine Learning field. The automation of the classification process can make the diagnosis process user-friendly and may also solve the problem related to regions where healthcare infrastructure remains a serious concern. Since the topic is relevant to the social cause, I would like to find a suitable research environment in future to continue the work based on my research theme. I see myself as an academician and would also like to distribute the knowledge to each human-being interested.

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