APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS

1st International Conference on Chemo and BioInformatics, ICCBIKG  2021, (47-54)

AUTHOR(S) / АУТОР(И): Zlatan Car, Nikola Anđelić, Ivan Lorencin, Jelena Musulin, Daniel Štifanić, Sandi Baressi Šegota

E-ADRESS / Е-АДРЕСА: car@riteh.hr , nandelic@riteh.hr , ilorencin@riteh.hr , jmusulin@riteh.hr , dstifanic@riteh.hr , sbaressisegota@riteh.hr

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DOI: 10.46793/ICCBI21.047C

ABSTRACT / САЖЕТАК:

The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.

KEY WORDS / КЉУЧНЕ РЕЧИ:

artificial intelligence, computer vision, machine learning, smart diagnostics

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