Affiliations: [a] Department of ECE, Karpagam College of Engineering, Coimbatore, Tamilnadu, India
| [b] Department of ECE, Rathinam Technical Campus, Coimbatore, Tamilnadu, India
| [c] Department of ECE, Hindusthan College of Engineering and Technology, Coimbatore, Tamilnadu, India
Correspondence:
[*]
Corresponding author. G. Premananthan, Department of ECE, Karpagam College of Engineering, Coimbatore, Tamilnadu, India. E-mail: premananthan2021@gmail.com.
Abstract: In recent times, ML algorithms that plays a significant role right from drug discovery to clinical decision making. The recent advances in DL technologies contribute towards improved performance for carrying out computer aided medical image analysis and disease diagnosis. The key benefit of AI in processing of medical big data offers spectacular insights into the hierarchal relationships that exist among data which can be algorithmically explored thus replacing the tedious manual processes to extract and localize specific areas of interests in medical images thus considerably changing the way medicine has been practiced so far. In bio medical related clinical applications, there is a constant demand pertaining the research and development with respect to deploying AI as a mainstream tool to perform several medical imaging activities like analysis, diagnosis, segmentation as well as classification. The increased usage of electronic health records and medical images being its integral component the need for appropriate and efficient AI assisted medical image analysis system that takes care of accurate and automated decision making could be of great help to radiologists and medical practitioners. Molecular image analysis is a dynamic field that makes use of ML and DL algorithms that utilizes labeled and structured information which also proves to be helpful to the patients as they serve as an initial interface before further diagnosis and treatments. Thus our research aims to offer a novel and efficient AI based medical analysis system that can assist clinical practitioners to focus on enhancing the disease diagnosis through DL based medical image analysis and decision making. In addition, we also address specific challenges related to disease diagnosis and propose novel GAN model for improved diagnosis and implementation. Our proposed technique can also be generalized to generate synthetic data for further issues related to molecular image analysis in the field of medicine and help towards building a better disease diagnosis model.
Keywords: Artificial Intelligence (AI), Deep learning (DL), electronic health records (EHR), Generative Adversarial networks (GAN), medical image analysis, Machine learning (ML).
