Generative Adversarial Networks for High-Resolution Medical Image Synthesis

Dr. Manik Kusal

Abstract


The availability of high-quality medical images is essential for training AI models in healthcare. However, data scarcity and privacy concerns often limit access to such datasets. This paper introduces a novel approach using Generative Adversarial Networks (GANs) to synthesize high-resolution medical images that maintain clinical relevance. The proposed model is evaluated on multiple imaging modalities, including MRI and CT scans, and demonstrates its ability to generate realistic images that augment existing datasets. The study also highlights the potential of GANs in reducing bias and improving diagnostic AI systems.

References


Davuluri, M. (2022). Comparative Study of Machine Learning Algorithms in Predicting Diabetes Onset Using Electronic Health Records. Research-gate journal, 8(8).

Davuluri, M. (2020). AI-Driven Predictive Analytics in Patient Outcome Forecasting for Critical Care. Research-gate journal, 6(6).

Davuluri, M. (2018). Revolutionizing Healthcare: The Role of AI in Diagnostics, Treatment, and Patient Care Integration. International Transactions in Artificial Intelligence, 2(2).

Davuluri, M. (2018). Navigating AI-Driven Data Management in the Cloud: Exploring Limitations and Opportunities. Transactions on Latest Trends in IoT, 1(1), 106-112.

Vattikuti, M. C. (2022). Comparative Analysis of Deep Learning Models for Tumor Detection in Medical Imaging. Research-gate journal, 8(8).

Vattikuti, M. C. (2020). A Comprehensive Review of AI-Based Diagnostic Tools for Early Disease Detection in Healthcare. Research-gate journal, 6(6).

Vattikuti, M. C. (2018). Leveraging Edge Computing for Real-Time Analytics in Smart City Healthcare Systems. International Transactions in Artificial Intelligence, 2(2).

Vattikuti, M. C. (2018). Leveraging AI for Sustainable Growth in AgTech: Business Models in the Digital Age. Transactions on Latest Trends in IoT, 1(1), 100-105.

Vattikuti, M. C. (2017). Ethical Framework for Integrating IoT in Urban Healthcare Systems. International Transactions in Artificial Intelligence, 1(1).

Kolla, V. R. K. (2020). Forecasting the Future of Crypto currency: A Machine Learning Approach for Price Prediction. International Research Journal of Mathematics, Engineering and IT, 7(12).

Kolla, V. R. K. (2018). Forecasting the Future: A Deep Learning Approach for Accurate Weather Prediction. International Journal in IT & Engineering (IJITE).

Kolla, V. R. K. (2016). Analyzing the Pulse of Twitter: Sentiment Analysis using Natural Language Processing Techniques. International Journal of Creative Research Thoughts.

Kolla, V. R. K. (2015). Heart Disease Diagnosis Using Machine Learning Techniques In Python: A Comparative Study of Classification Algorithms For Predictive Modeling. International Journal of Electronics and Communication Engineering & Technology.

Kolla, V. R. K. (2020). Paws And Reflect: A Comparative Study of Deep Learning Techniques For Cat Vs Dog Image Classification. International Journal of Computer Engineering and Technology.

Kolla, V. R. K. (2016). Forecasting Laptop Prices: A Comparative Study of Machine Learning Algorithms for Predictive Modeling. International Journal of Information Technology & Management Information System.

Kolla, V. R. K. (2021). Cyber security operations centre ML framework for the needs of the users. International Journal of Machine Learning for Sustainable Development, 3(3), 11-20.

Velaga, S. P. (2014). DESIGNING SCALABLE AND MAINTAINABLE APPLICATION PROGRAMS. IEJRD-International Multidisciplinary Journal, 1(2), 10.

Velaga, S. P. (2016). LOW-CODE AND NO-CODE PLATFORMS: DEMOCRATIZING APPLICATION DEVELOPMENT AND EMPOWERING NON-TECHNICAL USERS. IEJRD-International Multidisciplinary Journal, 2(4), 10.

Velaga, S. P. (2017). “ROBOTIC PROCESS AUTOMATION (RPA) IN IT: AUTOMATING REPETITIVE TASKS AND IMPROVING EFFICIENCY. IEJRD-International Multidisciplinary Journal, 2(6), 9.

Velaga, S. P. (2018). AUTOMATED TESTING FRAMEWORKS: ENSURING SOFTWARE QUALITY AND REDUCING MANUAL TESTING EFFORTS. International Journal of Innovations in Engineering Research and Technology, 5(2), 78-85.

Velaga, S. P. (2020). AIASSISTED CODE GENERATION AND OPTIMIZATION: LEVERAGING MACHINE LEARNING TO ENHANCE SOFTWARE DEVELOPMENT PROCESSES. International Journal of Innovations in Engineering Research and Technology, 7(09), 177-186.

Gatla, T. R. An innovative study exploring revolutionizing healthcare with ai: personalized medicine: predictive diagnostic techniques and individualized treatment. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320-2882.

Gatla, T. R. ENHANCING CUSTOMER SERVICE IN BANKS WITH AI CHATBOTS: THE EFFECTIVENESS AND CHALLENGES OF USING AI-POWERED CHATBOTS FOR CUSTOMER SERVICE IN THE BANKING SECTOR (Vol. 8, No. 5). TIJER–TIJER–INTERNATIONAL RESEARCH JOURNAL (www. TIJER. org), ISSN: 2349-9249.

Gatla, T. R. (2017). A SYSTEMATIC REVIEW OF PRESERVING PRIVACY IN FEDERATED LEARNING: A REFLECTIVE REPORT-A COMPREHENSIVE ANALYSIS. IEJRD-International Multidisciplinary Journal, 2(6), 8.

Gatla, T. R. (2019). A CUTTING-EDGE RESEARCH ON AI COMBATING CLIMATE CHANGE: INNOVATIONS AND ITS IMPACTS. INNOVATIONS, 6(09).

Gatla, T. R. “A GROUNDBREAKING RESEARCH IN BREAKING LANGUAGE BARRIERS: NLP AND LINGUISTICS DEVELOPMENT. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320-2882.

Gatla, T. R. (2018). AN EXPLORATIVE STUDY INTO QUANTUM MACHINE LEARNING: ANALYZING THE POWER OF ALGORITHMS IN QUANTUM COMPUTING. International Journal of Emerging Technologies and Innovative Research (www. jetir. org), ISSN, 2349-5162.

Gatla, T. R. MACHINE LEARNING IN DETECTING MONEY LAUNDERING ACTIVITIES: INVESTIGATING THE USE OF MACHINE LEARNING ALGORITHMS IN IDENTIFYING AND PREVENTING MONEY LAUNDERING SCHEMES (Vol. 6, No. 7, pp. 4-8). TIJER–TIJER–INTERNATIONAL RESEARCH JOURNAL (www. TIJER. org), ISSN: 2349-9249.

Gatla, T. R. (2020). AN IN-DEPTH ANALYSIS OF TOWARDS TRULY AUTONOMOUS SYSTEMS: AI AND ROBOTICS: THE FUNCTIONS. IEJRD-International Multidisciplinary Journal, 5(5), 9.

Pindi, V. (2018). NATURAL LANGUAGE PROCESSING(NLP) APPLICATIONS IN HEALTHCARE: EXTRACTING VALUABLE INSIGHTS FROM UNSTRUCTURED MEDICAL DATA. International Journal of Innovations in Engineering Research and Technology, 5(3), 1-10.

Pindi, V. (2019). A AI-ASSISTED CLINICAL DECISION SUPPORT SYSTEMS: ENHANCING DIAGNOSTIC ACCURACY AND TREATMENT RECOMMENDATIONS. International Journal of Innovations in Engineering Research and Technology, 6(10), 1-10.

PINDI, V. (2022). ETHICAL CONSIDERATIONS AND REGULATORY COMPLIANCE IN IMPLEMENTING AI SOLUTIONS FOR HEALTHCARE APPLICATIONS. IEJRD-International Multidisciplinary Journal, 5(5), 11.

Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.

Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).

Deekshith, A. (2021). Data engineering for AI: Optimizing data quality and accessibility for machine learning models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.

Deekshith, A. (2022). Cross-Disciplinary Approaches: The Role of Data Science in Developing AI-Driven Solutions for Business Intelligence. International Machine learning journal and Computer Engineering, 5(5).

Deekshith, A. (2023). Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications. International Scientific Journal for Research, 5(5).

DEEKSHITH, A. (2018). Seeding the Future: Exploring Innovation and Absorptive Capacity in Healthcare 4.0 and HealthTech. Transactions on Latest Trends in IoT, 1(1), 90-99.

DEEKSHITH, A. (2017). Evaluating the Impact of Wearable Health Devices on Lifestyle Modifications. International Transactions in Artificial Intelligence, 1(1).

DEEKSHITH, A. (2016). Revolutionizing Business Operations with Artificial Intelligence, Machine Learning, and Cybersecurity. International Journal of Sustainable Development in computer Science Engineering, 2(2).

DEEKSHITH, A. (2015). Exploring the Foundations, Applications, and Future Prospects of Artificial Intelligence. International Journal of Sustainable Development in computer Science Engineering, 1(1).

DEEKSHITH, A. (2014). Neural Networks and Fuzzy Systems: A Synergistic Approach. Transactions on Latest Trends in Health Sector, 6(6).

Deekshith, A. (2023). Transfer Learning for Multilingual Speech Recognition in Low-Resource Languages. International Transactions in Machine Learning, 5(5).

Deekshith, A. (2021). AI-Driven Sentiment Analysis for Enhancing Customer Experience in E-Commerce. International Journal of Machine Learning for Sustainable Development, 3(2).

DEEKSHITH, A. (2019). From Clinics to Care: A Technological Odyssey in Healthcare and Medical Manufacturing. Transactions on Latest Trends in IoT, 2(2).

DEEKSHITH, A. (2018). Integrating IoT into Smart Cities: Advancing Urban Health Monitoring and Management. International Transactions in Artificial Intelligence, 2(2).

DEEKSHITH, A. (2016). Revolutionizing Business Operations with Artificial Intelligence, Machine Learning, and Cybersecurity. International Journal of Sustainable Development in computer Science Engineering, 2(2).

Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).

Boppiniti, S. T. (2023). Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science. International Scientific Journal for Research, 5(5).

BOPPINITI, S. T. (2018). Human-Centric Design for IoT-Enabled Urban Health Solutions: Beyond Data Collection. International Transactions in Artificial Intelligence, 2(2).

BOPPINITI, S. T. (2018). Unraveling the Complexities of Healthcare Data Governance: Strategies, Challenges, and Future Directions. Transactions on Latest Trends in IoT, 1(1), 73-89.

BOPPINITI, S. T. (2017). Privacy-Preserving Techniques for IoT-Enabled Urban Health Monitoring: A Comparative Analysis. International Transactions in Artificial Intelligence, 1(1).

BOPPINITI, S. T. (2016). Core Standards and Applications of Big Data Analytics. International Journal of Sustainable Development in computer Science Engineering, 2(2).

BOPPINITI, S. T. (2015). Revolutionizing Industries with Machine Learning: A Global Insight. International Journal of Sustainable Development in computer Science Engineering, 1(1).

BOPPINITI, S. T. (2014). Emerging Paradigms in Robotics: Fundamentals and Future Applications. Transactions on Latest Trends in Health Sector, 6(6).

Boppiniti, S. T. (2023). AI-Powered Disease Outbreak Prediction Using Environmental and Social Data. International Transactions in Machine Learning, 5(5).

Danda, R. R. (2023). Neural Network-Based Models For Predicting Healthcare Needs In International Travel Coverage Plans.

Danda, R. R. AI-Driven Incentives in Insurance Plans: Transforming Member Health Behavior through Personalized Preventive Care.

Danda, R. R. Digital Transformation In Agriculture: The Role Of Precision Farming Technologies.

Danda, R. R. (2022). Innovations in Agricultural Machinery: Assessing the Impact of Advanced Technologies on Farm Efficiency. Journal of Artificial Intelligence and Big Data, 2(1), 64-83.

Reddy, R. (2020). Predictive Modeling with AI and ML for Small Business Health Plans: Improving Employee Health Outcomes and Reducing Costs. Available at SSRN 5018069.

Reddy, R. (2022). Telehealth In Medicare Plans: Leveraging AI For Improved Accessibility And Senior Care Quality. Available at SSRN 5032655.

Danda, R. R., Yasmeen, Z., & Maguluri, K. K. AI-Driven Healthcare Transformation: Machine Learning, Deep Learning, and Neural Networks in Insurance and Wellness Programs. JEC PUBLICATION.

Madhura, R., Krishnappa, K. H., Shashidhar, R., Shwetha, G., Yashaswini, K. P., & Sandya, G. R. (2023, December). UVM Methodology for ARINC 429 Transceiver in Loop Back Mode. In 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-7). IEEE.

KRISHNAPPA, K. H., & Trivedi, S. K. (2023). Efficient and Accurate Estimation of Pharmacokinetic Maps from DCE-MRI using Extended Tofts Model in Frequency Domain.

Shashidhar, R., Aditya, V., Srihari, N., Subhash, M. H., & Krishnappa, K. H. (2023, November). Empowering Investors: Insights from Sentiment Analysis, FFT, and Regression in Indian Stock Markets. In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) (pp. 01-06). IEEE.

Madhura, R., Krishnappa, K. H., Manasa, R., & Yashaswini, K. P. (2023, August). Slack Time Analysis for APB Timer Using Genus Synthesis Tool. In International Conference on ICT for Sustainable Development (pp. 207-217). Singapore: Springer Nature Singapore.

Shashidhar, R., Aprameya, C. V., Bharadwaj, R. R., Gontamar, S. M., & Krishnappa, K. H. (2023, November). Seismic Signal Processing and Aftershock Analysis using Machine Learning. In 2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) (pp. 1-9). IEEE.

Krishnappa, K. H., Shashidhar, R., Shashank, M. P., & Roopa, M. (2023, November). Detecting Parkinson's Disease with Prediction: A Novel SVM Approach. In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) (pp. 1-7). IEEE.

Sarisa, M., Boddapati, V. N., Patra, G. K., Kuraku, C., & Konkimalla, S. (2022). Deep Learning Approaches To Image Classification: Exploring The Future Of Visual Data Analysis. Educational Administration: Theory and Practice, 28(4), 331-345.

Patra, G. K., Rajaram, S. K., Boddapati, V. N., Kuraku, C., & Gollangi, H. K. (2022). Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security. International Journal of Engineering and Computer Science, 11(08), 10-18535.

Sarisa, M., Boddapati, V. N., Patra, G. K., Kuraku, C., Konkimalla, S., & Rajaram, S. K. (2020). An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods. Journal of Artificial Intelligence and Big Data, 1(1), 75-85.

Galla, E. P., Gollangi, H. K., Boddapati, V. N., Sarisa, M., Polimetla, K., Rajaram, S. K., & Reddy, M. S. (2023). Prediction of Financial Stock Market Based on Machine Learning Technique. Eswar Prasad G, Hemanth Kumar G, Venkata Nagesh B, Manikanth S, Kiran P, et al.(2023) Prediction of Financial Stock Market Based on Machine Learning Technique. J Contemp Edu Theo Artific Intel: JCETAI-102.

Galla, E. P., Gollangi, H. K., Boddapati, V. N., Sarisa, M., Polimetla, K., Rajaram, S. K., & Reddy, M. S. (2023). Enhancing Performance of Financial Fraud Detection Through Machine Learning Model. Eswar Prasad G, Hemanth Kumar G, Venkata Nagesh B, Manikanth S, Kiran P, et al.(2023) Enhancing Performance of Financial Fraud Detection Through Machine Learning Model. J Contemp Edu Theo Artific Intel: JCETAI-101.

Gollangi, H. K., Bauskar, S. R., Madhavaram, C. R., Galla, E. P., Sunkara, J. R., & Reddy, M. S. (2020). Unveiling the Hidden Patterns: AI-Driven Innovations in Image Processing and Acoustic Signal Detection. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 8(1), 25-45.

Gollangi, H. K., Bauskar, S. R., Madhavaram, C. R., Galla, E. P., Sunkara, J. R., & Reddy, M. S. (2020). Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records. Journal of Artificial Intelligence and Big Data, 1(1), 65-74.

Bauskar, S., Boddapati, V. N., Sarisa, M., Reddy, M., Sunkara, J. R., Rajaram, S. K., & Polimetla, K. (2022). Data Migration in the Cloud Database: A Review of Vendor Solutions and Challenges. Available at SSRN 4988789.

Madhavaram, C. R., Galla, E. P., Reddy, M. S., Sarisa, M., & Nagesh, V. (2021). Predicting Diabetes Mellitus in Healthcare: A Comparative Analysis of Machine Learning Algorithms on Big Dataset. Journal homepage: https://gjrpublication. com/gjrecs, 1(01).

Galla, P., Sunkara, R., & Reddy, S. (2020). ECHOES IN PIXELS: THE INTERSECTION OF IMAGE PROCESSING AND SOUND DETECTION THROUGH THE LENS OF AI AND ML.

Bauskar, S. R., Reddy, M. S., Sarisa, M., & KONKIMALLA, S. The Future of Cloud Computing_ Al-Driven Deep Learning and Neural Network Innovations. BUDHA PUBLISHER.

Konkimalla, S., SARISA, M., REDDY, M. S., & BAUSKAR, S. DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED. BUDHA PUBLISHER.

Adusumilli, S., Damancharla, H., & Metta, A. (2020). Artificial Intelligence-Driven Predictive Analytics for Educational Behavior Assessment. Transactions on Latest Trends in Artificial Intelligence, 1(1). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/638

Adusumilli, S., Damancharla, H., & Metta, A. (2020). Machine Learning Algorithms for Fraud Detection in Financial Transactions. International Journal of Sustainable Development in Computing Science, 2(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/639

Adusumilli, S., Damancharla, H., & Metta, A. (2020). Leveraging AI for Real-Time Sentiment Analysis in Social Media Networks. (2020). International Numeric Journal of Machine Learning and Robots, 4(4). https://injmr.com/index.php/fewfewf/article/view/182

AI-Powered Cybersecurity Solutions for Threat Detection and Prevention (S. B. K. Adusumilli, H. Damancharla, & A. R. Metta , Trans.). (2021). International Journal of Creative Research In Computer Technology and Design, 3(3). https://jrctd.in/index.php/IJRCTD/article/view/74

Adusumilli, S., Damancharla, H., & Metta, A. (2021). Deep Learning Techniques for Image Recognition in Autonomous Vehicles. (2021). International Meridian Journal, 3(3). https://meridianjournal.in/index.php/IMJ/article/view/94

Adusumilli, S., Damancharla, H., & Metta, A. (2021). Integrating Machine Learning and Blockchain for Decentralized Identity Management Systems. (2021). International Journal of Machine Learning and Artificial Intelligence, 2(2). https://jmlai.in/index.php/ijmlai/article/view/46

Adusumilli, S., Damancharla, H., & Metta, A. (2022). Blockchain-Based Secure Framework for IoT Data Management. International Journal of Sustainable Development in Computing Science, 4(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/640

Adusumilli, S., Damancharla, H., & Metta, A. (2022). Optimizing Supply Chain Efficiency Through Blockchain and Smart Contracts. (2022). International Numeric Journal of Machine Learning and Robots, 6(6). https://injmr.com/index.php/fewfewf/article/view/183

Adusumilli, S., Damancharla, H., & Metta, A. (2023). Enhancing Data Privacy in Healthcare Systems Using Blockchain Technology. Transactions on Latest Trends in Artificial Intelligence, 4(4). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/637

Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021). Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model.


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