Transforming Cancer Diagnostics and Personalized Treatment with Deep Learning Models

DOI

10.2991/978-94-6463-738-0_8How to use a DOI?

Keywords

Disease Progression; AI-Driven Healthcare; Cancer Diagnosis; Deep Learning; Histopathological Image Analysis; Therapeutic Optimization

Abstract

Advances in deep learning have fundamentally changed how cancer is diagnosed and treated. These cutting-edge models show great promise for boosting therapeutic approaches and increasing diagnostic accuracy, launching a new age of individualized cancer treatment. These days, sophisticated methods like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are widely used to assess complicated medical data, such as genetic information and histopathological images. By providing fresh perspectives on disease causes, these AI-powered techniques make it possible to develop individualized treatment programs that take into consideration the distinct genetic and physiological traits of every patient. They also improve forecasts of the course of diseases and the effectiveness of treatments, which makes precision medicine easier to apply. The integration of deep learning into cancer treatment regimens is examined in this study, with a focus on how it might improve clinical decision-making and analyze a variety of datasets. When analyzing histopathology slides, CNNs have been extremely helpful in accurately identifying and classifying tumors. Concurrently, the analysis of sequential genomic data has been enhanced by RNNs and Long Short-Term Memory (LSTM) networks, which have identified significant patterns linked to cancer mutations. The study also discusses current advancements, challenges, and potential future directions in this area, emphasizing how deep learning is revolutionizing clinical oncology. It is anticipated that these developments would enhance patient outcomes and usher in a new era of AI-powered healthcare.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - G. S. Pradeep Ghantasala
AU  - Nalli Vinaya Kumari
AU  - R. Rajesh Sharma
AU  - Pellakuri Vidyullatha
AU  - Akey Sungheetha
AU  - Gaganpreet Kaur
PY  - 2025
DA  - 2025/06/22
TI  - Transforming Cancer Diagnostics and Personalized Treatment with Deep Learning Models
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 90
EP  - 102
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_8
DO  - 10.2991/978-94-6463-738-0_8
ID  - Ghantasala2025
ER  -