Case Study: AI-Driven Early Detection of Cancer Using Deep Learning Models
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I4P105Keywords:
AI In Healthcare, Deep Learning, Cancer Detection, Biopsy Analysis, Medical Imaging, Early Diagnosis, Convolutional Neural Networks (Cnns), Histopathology, AI-Assisted Radiology, Tumor Segmentation, Machine Learning In Oncology, Diagnostic Accuracy, Clinical AI Adoption, Regulatory Challenges, AI Bias In Healthcare, Ethical AI, Federated Learning, Medical Dataset Diversity, AI-Driven Diagnostics, Automated Cancer ScreeningAbstract
Artificial intelligence (AI) is transforming healthcare in the field of their early cancer detection, where time is usually the most important determinant of life preservation. This case study investigates, incorporating biopsies, X-rays & also MRIs, the use of DL models for the exact identification of malignant patterns in medical imaging. By use of huge scale databases, computer algorithms might spot anomalies that can elude human awareness, hence greatly improving early diagnosis rates. This study mostly looks at how AI is used in hospitals, stressing both achievements & useful challenges faced. Though AI-driven solutions have great promise, obstacles include algorithmic bias, legal restrictions & the doctors' resistance to adopt new technologies limit more general adoption. The requirement of thorough validation & openness in AI models to ensure the efficacy & confidence in their therapeutic surroundings is investigated in this work. Notwithstanding these constraints, artificial intelligence unequivocally presents possible benefits in cancer detection: faster and more accurate results, less diagnosis errors, and finally improved patient outcomes. AI may become a necessary tool in fighting cancer as technology develops and rules change, improving medical practitioner competency rather than replacing them. This case study clarifies the present situation of AI-driven cancer diagnosis & provides understanding of a time when human knowledge and technology will cooperate to save lives
References
[1] Kupunarapu, Sujith Kumar. "Data Fusion and Real-Time Analytics: Elevating Signal Integrity and Rail System Resilience." International Journal of Science And Engineering 9.1 (2023): 53-61.
[2] Kupunarapu, Sujith Kumar. "AI-Driven Crew Scheduling and Workforce Management for Improved Railroad Efficiency." International Journal of Science And Engineering 8.3 (2022): 30-37.
[3] Kupunarapu, Sujith Kumar. "AI-Enhanced Rail Network Optimization: Dynamic Route Planning and Traffic Flow Management." International Journal of Science And Engineering 7.3 (2021): 87-95.
[4] Kupunarapu, Sujith Kumar. "AI-Enabled Remote Monitoring and Telemedicine: Redefining Patient Engagement and Care Delivery." International Journal of Science And Engineering 2.4 (2016): 41-48.
[5] Chaganti, Krishna Chiatanya. "Securing Enterprise Java Applications: A Comprehensive Approach." International Journal of Science And Engineering 10.2 (2024): 18-27.
[6] Chaganti, Krishna Chaitanya. "AI-Powered Threat Detection: Enhancing Cybersecurity with Machine Learning." International Journal of Science And Engineering 9.4 (2023): 10-18.
[7] Chaganti, Krishna Chaitanya. "The Role of AI in Secure DevOps: Preventing Vulnerabilities in CI/CD Pipelines." International Journal of Science And Engineering 9.4 (2023): 19-29.
[8] Chaganti, Krishna C. "Advancing AI-Driven Threat Detection in IoT Ecosystems: Addressing Scalability, Resource Constraints, and Real-Time Adaptability."
[9] Chaganti, Krishna. "Adversarial Attacks on AI-driven Cybersecurity Systems: A Taxonomy and Defense Strategies." Authorea Preprints.
[10] Chaganti, Krishna C. "Leveraging Generative AI for Proactive Threat Intelligence: Opportunities and Risks." Authorea Preprints.
[11] Sangaraju, Varun Varma. "UI Testing, Mutation Operators, And the DOM in Sensor-Based Applications."
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." Nutrition and Obsessive-Compulsive Disorder. CRC Press 26-35.
[12] Sangaraju, Varun Varma. "Optimizing Enterprise Growth with Salesforce: A Scalable Approach to Cloud-Based Project Management." International Journal of Science And Engineering 8.2 (2022): 40-48.
[13] Sangaraju, Varun Varma. "AI-Augmented Test Automation: Leveraging Selenium, Cucumber, and Cypress for Scalable Testing." International Journal of Science And Engineering 7.2 (2021): 59-68.
[14] Sangaraju, Varun Varma. "Ranking Of XML Documents by Using Adaptive Keyword Search." (2014): 1619-1621.
[15] Sreedhar, C., and Varun Verma Sangaraju. "A Survey On Security Issues In Routing In MANETS." International Journal of Computer Organization Trends 3.9 (2013): 399-406.
[16] Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Danio rerio: A Promising Tool for Neurodegenerative Dysfunctions." Animal Behavior in the Tropics: Vertebrates: 47.
[17] Immaneni, J. "Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success." Innovative Computer Sciences Journal 6.1 (2020).
[18] Immaneni, Jayaram. "Using Swarm Intelligence and Graph Databases Together for Advanced Fraud Detection." Journal of Big Data and Smart Systems 1.1 (2020).
[19] Immaneni, J. "Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success." Innovative Computer Sciences Journal 6.1 (2020).
[20] Immaneni, Jayaram. "Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection." Journal of Computational Innovation 1.1 (2021).
[21] Immaneni, Jayaram. "Scaling Machine Learning in Fintech with Kubernetes." International Journal of Digital Innovation 2.1 (2021).
[22] Immaneni, Jayaram. "Securing Fintech with DevSecOps: Scaling DevOps with Compliance in Mind." Journal of Big Data and Smart Systems 2.1 (2021).
[23] Shaik, Babulal, and Jayaram Immaneni. "Enhanced Logging and Monitoring With Custom Metrics in Kubernetes." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 307-30.
[24] Boda, V. V. R., and J. Immaneni. "Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen." Innovative Computer Sciences Journal 7.1 (2021).
[25] Immaneni, Jayaram. "End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes." Journal of Computational Innovation 2.1 (2022).
[26] Immaneni, Jayaram. "Strengthening Fraud Detection with Swarm Intelligence and Graph Analytics." International Journal of Digital Innovation 3.1 (2022).
[27] Immaneni, Jayaram. "Practical Cloud Migration for Fintech: Kubernetes and Hybrid-Cloud Strategies." Journal of Big Data and Smart Systems 3.1 (2022).
[28] Boda, V. V. R., and J. Immaneni. "Optimizing CI/CD in Healthcare: Tried and True Techniques." Innovative Computer Sciences Journal 8.1 (2022).
[29] Immaneni, Jayaram. "Detecting Complex Fraud with Swarm Intelligence and Graph Database Patterns." Journal of Computing and Information Technology 3.1 (2023).
[30] Boda, V. V. R., and J. Immaneni. "Automating Security in Healthcare: What Every IT Team Needs to Know." Innovative Computer Sciences Journal 9.1 (2023).
[31] Boda, V. V. R., and H. Allam. "Scaling Up with Kubernetes in FinTech: Lessons from the Trenches." Innovative Computer Sciences Journal 5.1 (2019).
[32] Boda, V. V. R., and H. Allam. "Crossing Over: How Infrastructure as Code Bridges FinTech and Healthcare." Innovative Computer Sciences Journal 6.1 (2020).
[33] Boda, Vishnu Vardhan Reddy, and Hitesh Allam. "Automating Compliance in Healthcare: Tools and Techniques You Need." Innovative Engineering Sciences Journal 1.1 (2021).
[34] Boda, V. V. R., and H. Allam. "Ready for Anything: Disaster Recovery Strategies Every Healthcare IT Team Should Know." Innovative Engineering Sciences Journal 2.1 (2022).
[35] Boda, V. V. R., and H. Allam. "Scaling Kubernetes for Healthcare: Real Lessons from the Field." Innovative Engineering Sciences Journal 3.1 (2023).
[36] Katari, Abhilash, Anirudh Muthsyala, and Hitesh Allam. "HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES."
[37] Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
[38] Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
[39] Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
[40] Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
[41] Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
[42] Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
[43] Muneer Ahmed Salamkar. Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, Mar. 2023, pp. 668-94
[44] Muneer Ahmed Salamkar, et al. Data Transformation and Enrichment: Utilizing ML to Automatically Transform and Enrich Data for Better Analytics. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, July 2023, pp. 613-38
[45] Muneer Ahmed Salamkar. Real-Time Analytics: Implementing ML Algorithms to Analyze Data Streams in Real-Time. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, Sept. 2023, pp. 587-12
[46] Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
[47] Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
[48] Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
[49] Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
[50] Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
[51] Piyushkumar Patel. “Adapting to the SEC’s New Cybersecurity Disclosure Requirements: Implications for Financial Reporting”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 1, Jan. 2023, pp. 883-0
[52] Piyushkumar Patel, et al. “Accounting for NFTs and Digital Collectibles: Establishing a Framework for Intangible Asset ”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, Mar. 2023, pp. 716-3
[53] Piyushkumar Patel, and Deepu Jose. “Preparing for the Phased-Out Full Expensing Provision: Implications for Corporate Capital Investment Decisions ”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, May 2023, pp. 699-18.
[54] Piyushkumar Patel. “Accounting for Climate-Related Contingencies: The Rise of Carbon Credits and Their Financial Reporting Impact”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 1, June 2023, pp. 490-12
[55] Piyushkumar Patel. “The Role of Central Bank Digital Currencies (CBDCs) in Corporate Financial Strategies and Reporting”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, Sept. 2023, pp. 1194-1
[56] Piyushkumar Patel. “The Implementation of Pillar Two: Global Minimum Tax and Its Impact on Multinational Financial Reporting”. Australian Journal of Machine Learning Research & Applications, vol. 1, no. 2, Dec. 2021, pp. 227-46
[57] Piyushkumar Patel, et al. “Leveraging Predictive Analytics for Financial Forecasting in a Post-COVID World”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, Jan. 2021, pp. 331-50
[58] Piyushkumar Patel. “Navigating PPP Loan Forgiveness: Accounting Challenges and Tax Implications for Small Businesses”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Mar. 2021, pp. 611-34
[59] Piyushkumar Patel, et al. “Accounting for Supply Chain Disruptions: From Inventory Write-Downs to Risk Disclosure”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, May 2021, pp. 271-92
[60] Piyushkumar Patel. “Transfer Pricing in a Post-COVID World: Balancing Compliance With New Global Tax Regimes”. Australian Journal of Machine Learning Research & Applications, vol. 1, no. 2, July 2021, pp. 208-26
[61] Piyushkumar Patel. “The Corporate Transparency Act: Implications for Financial Reporting and Beneficial Ownership Disclosure”. Journal of Artificial Intelligence Research and Applications, vol. 2, no. 1, Apr. 2022, pp. 489-08
[62] Piyushkumar Patel, et al. “Navigating the BEAT (Base Erosion and Anti-Abuse Tax) under the TCJA: The Impact on Multinationals’ Tax Strategies”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 2, Aug. 2022, pp. 342-6
[63] Piyushkumar Patel. “Robotic Process Automation (RPA) in Tax Compliance: Enhancing Efficiency in Preparing and Filing Tax Returns”. African Journal of Artificial Intelligence and Sustainable Development, vol. 2, no. 2, Dec. 2022, pp. 441-66
[64] Piyushkumar Patel, and Hetal Patel. “Lease Modifications and Rent Concessions under ASC 842: COVID-19’s Lasting Impact on Lease Accounting”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Aug. 2020, pp. 824-41.
[65] Piyushkumar Patel. “Remote Auditing During the Pandemic: The Challenges of Conducting Effective Assurance Practices”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Oct. 2020, pp. 806-23
[66] Ravi Teja Madhala, and Sateesh Reddy Adavelli. “Blockchain for Fraud Detection in P&C Insurance Claims”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, Jan. 2023, pp. 740-66
[67] Ravi Teja Madhala. “Artificial Intelligence for Predictive Underwriting in P&C Insurance”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 1, Mar. 2023, pp. 513-37
[68] Ravi Teja Madhala, et al. “Cybersecurity Risk Modeling in P&C Insurance”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 1, Mar. 2023, pp. 925-49
[69] Ravi Teja Madhala. “Smart Contracts in P&C Insurance: Opportunities and Challenges”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, July 2023, pp. 708-33
[70] Ravi Teja Madhala, and Sateesh Reddy Adavelli. “AI-Powered Risk Assessment in Natural Catastrophe Insurance”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 2, Sept. 2023, pp. 842-67
[71] Ravi Teja Madhala. “Climate Risk Insurance: Addressing the Challenges and Opportunities in a Changing World”. Journal of Artificial Intelligence Research and Applications, vol. 2, no. 2, Dec. 2022, pp. 610-31
[72] Ravi Teja Madhala, and Nivedita Rahul. “Usage-Based Insurance (UBI): Leveraging Telematics for Dynamic Pricing and Customer-Centric Models ”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 320-42
[73] Ravi Teja Madhala, and Sateesh Reddy Adavelli. “The Role of AI and Machine Learning in Revolutionizing Underwriting Practices: Enhancing Risk Assessment, Decision-Making, and Operational Efficiency”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, May 2022, pp. 590-11
[74] Nookala, G., et al. "Zero-Trust Security Frameworks: The Role of Data Encryption in Cloud Infrastructure." MZ Computing Journal 4.1 (2023).
[75] Nookala, G., et al. "Integrating Data Warehouses with Data Lakes: A Unified Analytics Solution." Innovative Computer Sciences Journal 9.1 (2023.
[76] Nookala, G., et al. "Evolving from Traditional to Graph Data Models: Impact on Query Performance." Innovative Engineering Sciences Journal 3.1 (2023).
[77] Nookala, Guruprasad. "Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis." Journal of Computational Innovation 3.1 (2023).
[78] Nookala, Guruprasad. "Microservices and Data Architecture: Aligning Scalability with Data Flow." International Journal of Digital Innovation 4.1 (2023).
[79] Nookala, Guruprasad. "Secure Multiparty Computation (SMC) for Privacy-Preserving Data Analysis." Journal of Big Data and Smart Systems 4.1 (2023)
[80] Gade, Kishore Reddy. "Data Governance and Risk Management: Mitigating Data-Related Threats." Advances in Computer Sciences 3.1 (2020).
[81] Gade, Kishore Reddy. "Migrations: Cloud Migration Strategies, Data Migration Challenges, and Legacy System Modernization." Journal of Computing and Information Technology 1.1 (2021).
[82] Gade, Kishore Reddy. "Event-Driven Data Modeling in Fintech: A Real-Time Approach." Journal of Computational Innovation 3.1 (2023).
[83] Gade, Kishore Reddy. "The Role of Data Modeling in Enhancing Data Quality and Security in Fintech Companies." Journal of Computing and Information Technology 3.1 (2023).
[84] Sairamesh Konidala. “What Is a Modern Data Pipeline and Why Is It Important?”. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Dec. 2016, pp. 95-111
[85] Sairamesh Konidala, et al. “The Impact of the Millennial Consumer Base on Online Payments”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 154-71
[86] Sairamesh Konidala. “What Are the Key Concepts, Design Principles of Data Pipelines and Best Practices of Data Orchestration”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Jan. 2017, pp. 136-53
[87] Sairamesh Konidala, et al. “Optimizing Payments for Recurring Merchants”. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Aug. 2018, pp. 295-11
[88] Sairamesh Konidala, et al. “A Data Pipeline for Predictive Maintenance in an IoT-Enabled Smart Product: Design and Implementation”. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Mar. 2018, pp. 278-94
[89] Sairamesh Konidala. “Ways to Fight Online Payment Fraud”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Oct. 2019, pp. 1604-22
[90] Sairamesh Konidala. “Cloud-Based Data Pipelines: Design, Implementation and Example”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019, pp. 1586-03.
