AI in Oncology: A Review of Deep Learning-Based Approaches for Women’s Cancer Diagnosis

Naveen Kumar Kedia, Dr. Vijay Kumar

Deep Learning, Women’s Cancer Diagnosis, Breast Cancer, Cervical Cancer, Ovarian Cancer, CNN, Medical Imaging, Histopathology.

Cancer is among the leading causes of death among women worldwide and the most common types are breast, cervical, ovarian, and uterine cancers. It is crucial that these cancers be diagnosed early and accurately to help improve survival and treatment outcomes. Artificial intelligence (AI), with deep learning specifically, has come to be regarded as a transforming tool in the field of oncology, one that provides high diagnostic accuracy and automation with high efficiency. This review offers a comprehensive view of deep learning-based approaches for women's cancer detection and diagnosis, focusing on a variety of methodologies of AI applied in medical imaging, histopathology, and genomic analysis. The paperdiscusses several widely used deep learning architectures like CNNs, RNNs, transformer-based models, and hybrid techniques and their applications in identifying and categorizing various cancers in women. We also discuss some of the most important public datasets, performance metrics, and comparative evaluations of existing AI-driven diagnostic models. Even though there has been tremendous progress, data scarcity, model interpretability, ethical concerns, and integration into clinical workflows are critical barriers to adoption. The Paper also highlight emerging research trends, such as explainable AI, federated learning, and multi-modal fusion techniques, that aim to enhance the reliability and robustness of AI in oncology.This review synthesizes recent developments in an attempt to provide insights into the current landscape of AI-driven cancer diagnosis and identify future directions for research and clinical implementation. The findings point to the revolutionary potential of deep learning in the detection of women's cancers, but underscore the importance of interdisciplinary collaboration to overcome the limitations identified and translate AI advances into real-world healthcare solutions.

Received: 27 Aug 2024, Receive in revised form: 21 Sep 2024, Accepted: 26 Sep 2024, Available online: 30 Sep 2024

10.22161/ijaers.119.6