Deep Learning for Medical Image Analysis Applications in Disease Detection and Diagnosis

¹ Lecturer, Bengal College of Engineering and Technology (Diploma Engineering Division), SSB Sarani, Bidhannagar, Durgapur, West Bengal, India
² Associate Professor, Department of CSE, MLR Institute of Technology, Dundigal, Hyderabad, Telengana, India
³ Assistant Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, Tamil Nadu, India
⁴ Assistant Professor, CSE, J.J. College of Engineering and Technology, Trichy, Tamil Nadu, India
⁵ Associate Professor, Department of Mathematics, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, Tamil Nadu, India
⁶ Assistant Professor, Department of IT, New Prince Shri Bhavani College of Engineering and Technology Chennai, Tamil Nadu, India

swagata1916@gmail.com
nallashirisha@mlrinstitutions.ac.in
sureshhkumar.ssk@gmail.com
harthyrubypriyas@jjcet.ac.in
vanajar.sse@saveetha.com
mahima.m@newprinceshribhavani.com

Abstract

AI (machine learning or deep learning all belong to AI) has phenomenal potential in revolutionizing healthcare such as enhanced diagnostic precision, personalized treatment, better-quality patient outcomes along with cost reduction, etc. Its potential applications are indeed great, but realising its potential has been slow owing to a host of challenges such as implementing it in a real-life scenario, data privacy challenges, ethical concerns, AI models being biased, operating system structures needing to be interoperable with existing systems, and their compliance with regulating standards. The goal of this paper is therefore to formulate these barriers into an achievable framework for the practical adoption of AI in healthcare with specific focus on real world case studies, scalable solutions, and debiasing approaches. It analyzes the possible integration of explainable AI (XAI) for greater transparency and confidence, identifies potential solutions for data security, and offers a set of recommendations for how to incorporate AI systems into existing healthcare infrastructures. It includes improvements for bias mitigation and fairness in AI, and provides economic viability analysis of AI adoption, as well as clinical validation for AI models among others. These insights enable actionable guidance to position healthcare organizations to harness the power of AI to improve not only patient experience and outcomes, but also reduce the cost of care, while focusing on the ethical, safe and equitable use of the technology.