Recent Advancements in Artificial Intelligence for Breast Cancer: Image Augmentation, Segmentation, Diagnosis, and Prognosis Approaches

Nitish Verma; Ayush Sharma; Kelvin Luntsi; Anshika Varshney

doi:10.36948/ijfmr.2026.v08i05.87962

Recent Advancements in Artificial Intelligence for Breast Cancer: Image Augmentation, Segmentation, Diagnosis, and Prognosis Approaches

Author(s)	Mr. Nitish Verma, Mr. Ayush Sharma, Mr. Kelvin Luntsi, Ms. Anshika Varshney
Country	India
Abstract	Breast cancer remains the most frequently diagnosed malignancy and a leading cause of cancer death among women worldwide, with GLOBOCAN 2022 estimating 2.30 million new cases and 666,103 deaths globally (Bray et al., 2024; Gu et al., 2026). Artificial intelligence (AI), and deep learning (DL) in particular, has become central to efforts to improve early detection, diagnostic accuracy, and outcome prediction across the breast-imaging pipeline. This review synthesizes recent, peer-reviewed literature across four interconnected stages of the AI pipeline for breast cancer: (a) image augmentation, principally through generative adversarial networks (GANs) that mitigate small and imbalanced datasets; (b) semantic segmentation of tumor regions in ultrasound, mammographic, and magnetic resonance images using U-Net–based architectures; (c) diagnosis and classification of lesions as benign or malignant using convolutional neural networks (CNNs), transfer learning, and AI-assisted computer-aided diagnosis (CAD) systems applied to mammography, ultrasound, and histopathology; and (d) prognosis and survival prediction using multimodal and multi-omics deep learning. Reported performance metrics are summarized, including Dice similarity coefficients above 0.87 for state-of-the-art segmentation networks, classification accuracies exceeding 99% on benchmark histopathology datasets, and improvements in radiologist area-under-the-curve (AUC) from 0.84 to 0.91 with AI assistance (Abu Abeelh & Abuabeileh, 2025). The review also discusses persistent challenges — dataset scarcity, generalizability, interpretability, and clinical validation — and highlights emerging directions such as multimodal fusion and explainable AI. Findings indicate that while AI systems increasingly match or exceed human-level performance on narrow imaging tasks, translation into routine clinical workflows requires larger prospective trials, standardized reporting, and attention to fairness across breast densities and populations.
Keywords	artificial intelligence, deep learning, breast cancer, image augmentation, generative adversarial networks, segmentation, diagnosis, prognosis, mammography, ultrasound, histopathology
Field	Medical / Pharmacy
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-19
DOI	https://doi.org/10.36948/ijfmr.2026.v08i05.87962

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Recent Advancements in Artificial Intelligence for Breast Cancer: Image Augmentation, Segmentation, Diagnosis, and Prognosis Approaches

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