Matrix-Based Deep Learning Approach to AI-Driven Cancer Detection, Personalized Treatment, And Ethical Consideration

Authors

  • V. S. Saranya Assistant Professor, Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
  • Parasa Rajya Lakshmi Assistant professor, Department of Information Technology, Prasad V. Potluri Siddhartha Institute Of Technology, Kanuru, Vijayawada, 520007, Andhra Pradesh, India
  • Cheepurupalli Raghuram Assistant Professor, Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India
  • Syed Muqthadar Ali Senior Assistant Professor, CSE, CVR College of Engineering, Hyderabad, Telangana, India
  • Jayavarapu Karthik Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502, Andhra Pradesh, India
  • U. Ganesh Naidu Assistant Professor, CSBS department, B V Raju Institute of Technology, Narsapur, Medak, Telanagana, India.

Keywords:

artificial intelligence, personalized medicine, cancer detection, ethical considerations

Abstract

The field of cancer research and therapy has been revolutionized by the rapid development of artificial intelligence (AI). Focusing on early cancer diagnosis, individualized therapy, and addressing ethical problems, this study seeks to investigate the many ways in which AI-driven tools are expanding the frontiers of oncology. This study will revolutionize how early cancer is detected by analyzing imaging scans and diagnostic tests using artificial intelligence and machine learning algorithms. The study has mapped out automated approaches, with deep learning being the automatic classification crown gem due to its higher performance. Because of this, several deep learning network topologies have been created. In deep learning, selecting the best models to address a certain challenge is a major challenge. Therefore, an innovative ultrasonic image-based deep learning system based on a matrix dataset to choose the best performing networks for cancer detection automatically. Based on this information, the proposed system chooses amongst ResNet99, MobileNetX2, and EffNetb0 as the most suitable classification method. The precision of the developed model's categorization was 97.18% using 10-fold cross-validation. The goal of this project is to progress the field of oncology by addressing the significant ethical issues brought about by technological advancements which promise more precise and tailored cancer treatments.

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Published

30.11.2023

How to Cite

Saranya, V. S. ., Lakshmi, P. R. ., Raghuram, C. ., Ali, S. M. ., Karthik, J. ., & Naidu, U. G. . (2023). Matrix-Based Deep Learning Approach to AI-Driven Cancer Detection, Personalized Treatment, And Ethical Consideration . International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 112–120. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3963

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

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