Breast cancer diagnosis has greatly benefited from the use of deep learning (DL) and machine learning (ML) methods on multimodal data, including mammography, magnetic resonance imaging (MRI), ultrasound, histopathology, genomic data, and clinical reports. Every modality presents its challenges and opportunities, demanding a thorough understanding of used methods for accurate and early diagnosis. Our review provides an exhaustive survey of the latest advances in DL and ML applications to detect, classify, and segment breast cancer in these modalities. Our work also addresses basic issues of data limitations, model generalizability, and interpretability. To overcome these issues we explore the state-of-the-art Vision-Language Models (VLMs) as an emerging approach for multimodal learning. Through incorporation of the current advances and identification of areas of future improvement, this review is a precious source of information to researchers and practitioners. The findings highlight the importance of interdisciplinary collaboration, application of standardized data sets, and continuous improvement of the methodology to facilitate clinical uptake of AI-based breast cancer diagnostic systems.

Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional neural networks (CNNs) for automated mammogram classification is presented in this work, along with the introduction of two innovative multi-view integration techniques: Dual-Branch Ensemble (DBE) and Merged Dual-View (MDV). By setting aside two datasets for out-of-sample testing, we evaluate the generalizability of the model using six different mammography datasets that represent various populations and imaging systems. We compare a number of cutting-edge architectures on both individual and combined datasets, including ResNet, DenseNet, EfficientNet, MobileNet, Vision Transformers, and VGG19. Both MDV and DBE strategies improve classification performance, according to experimental results. VGG19 and DenseNet both obtained high ROC AUC scores of 0.9051 and 0.7960 under the MDV approach. DenseNet demonstrated strong performance in the DBE setting, achieving a ROC AUC of 0.8033, while ResNet50 recorded a ROC AUC of 0.8042. These enhancements demonstrate how beneficial multi-view fusion is for boosting model robustness. The impact of domain shift is further highlighted by generalization tests, which emphasize the need for diverse datasets in training. These results offer practical advice for improving CNN architectures and integration tactics, which will aid in the creation of trustworthy, broadly applicable AI-assisted breast cancer screening tools.

Early detection of breast cancer via mammography enhances patient survival rates, prompting this study to assess object detection models—Cascade R-CNN, YOLOv12 (S, L, and X variants), RTMDet-X, and RT-DETR-X—for detecting masses and calcifications across four public datasets (INbreast, CBIS-DDSM, VinDr-Mammo, and EMBED). The evaluation employs a standardized preprocessing approach (CLAHE, cropping) and augmentation (rotations, scaling), with transfer learning tested by training on combined datasets (e.g., INbreast + CBIS-DDSM) and validating on held-out sets (e.g., VinDr-Mammo). Performance is measured using precision, recall, mean Average Precision at IoU 0.5 (${\mathrm{mAP}}_{50}$), and F1-score. YOLOv12-L excels in mass detection with an ${\mathrm{mAP}}_{50}$ of 0.963 and F1-score up to 0.917 on INbreast, while RTMDet-X achieves an ${\mathrm{mAP}}_{50}$ of 0.697 on combined datasets with transfer learning. Preprocessing improves ${\mathrm{mAP}}_{50}$ by up to 0.209, and transfer learning elevates INbreast performance to an ${\mathrm{mAP}}_{50}$ of 0.995, though it incurs 5–11% drops on CBIS-DDSM (0.566 to 0.447) and VinDr-Mammo (0.59 to 0.5) due to domain shifts. EMBED yields a low ${\mathrm{mAP}}_{50}$ of 0.306 due to label inconsistencies, and calcification detection remains weak (${\mathrm{mAP}}_{50}$ < 0.116), highlighting the value of high-capacity models, preprocessing, and augmentation for mass detection while identifying calcification detection and domain adaptation as key areas for future investigation.

Breast cancer is one of the main causes of cancer-related death for women worldwide, and enhancing patient outcomes still depends on early detection. The most common imaging technique for diagnosing and screening for breast cancer is mammography, which has a high potential for early lesion detection. With an emphasis on the incorporation of deep learning (DL) techniques, this review examines the changing role of mammography in early breast cancer detection. We examine recent advancements in DL-based approaches for mammogram analysis, including tasks such as classification, segmentation, and lesion detection. Additionally, we assess the limitations of traditional mammographic methods and highlight how DL can enhance diagnostic accuracy, reduce false positives and negatives, and support clinical decision-making. The review emphasizes the potential of DL to assist radiologists in clinical decision-making, as well as increases in diagnostic accuracy and decreases in false positives and negatives. We also discuss issues like interpretability, generalization across populations, and data scarcity. This review summarizes the available data to highlight the revolutionary potential of DL-enhanced mammography in breast cancer screening and to suggest future research avenues for more reliable, transparent, and clinically useful AI-driven solutions.

Many historical handwritten records in low-resource languages remain difficult to access through modern digital systems. This limits efforts to preserve and study cultural heritage at scale. Chagatai manuscripts exemplify these challenges within the Eastern Turki tradition. For centuries, it served as a major written language across Central Asia and supported a rich literary tradition. Large collections of Chagatai manuscripts still survive today, yet only a small amount of this material exists in digital form. As the technical literature specifically focused on Chagatai-HTR remains in its nascent stage, this review synthesizes indirect evidence from taxonomically related Perso-Arabic scripts to establish a foundational research framework. This article presents a systematic literature review following the PRISMA guidelines to examine artificial intelligence methods for handwritten text recognition (HTR) and text restoration in low-resource languages. Analyzing 50 studies published between 2020 and 2026, the review categorizes research trends into handwritten text recognition (HTR), optical character recognition (OCR), script classification, dataset development, and multimodal vision–language systems. The findings reveal a significant architectural shift from traditional segmentation-based CNN and RNN models toward transformer architectures and multimodal approaches. However, for Chagatai specifically, the primary obstacle is not the lack of advanced models but a critical scarcity of basic research infrastructure, including expert-verified transcriptions, annotation standards, and open benchmark datasets. Consequently, this article proposes a concrete development roadmap focusing on systematic digitization, expert annotation, transfer learning, and the creation of baseline models to enable reproducible evaluations.

Breast cancer is a critical global health challenge, and deep learning shows transformative potential for medical image classification. However, privacy regulations such as HIPAA and GDPR create barriers to centralized data aggregation across institutions. This paper presents an empirical evaluation of federated learning (FL) for breast cancer classification in ultrasound images, systematically comparing seven deep learning architectures (ResNet-50, VGG16, VGG19, DenseNet-121, MobileNetV2, Vision Transformer, CoAtNet) across three FL algorithms (FedAvg, FedProx, FedOpt) with client-side differential privacy (DP). Using a simulated federation of eight institutions, we evaluate three clinically relevant classification scenarios. Federated models achieve performance comparable to centralized baselines—98.52% accuracy for normal/abnormal screening, 89.53% for three-class classification—with ViT-small and DenseNet-121 exceeding their centralized counterparts in several configurations. Under strong DP constraints (noise multiplier $\eta =2.0$, yielding conservative privacy budget estimates of $\epsilon <1.0$ with $\delta ={10}^{-5}$), screening accuracy remains above 82%, though diagnostic tasks incur substantial degradation (best 68.42%). Our findings provide empirical guidance on architecture selection, FL algorithm choice, and privacy-utility trade-offs for privacy-preserving breast cancer diagnosis, while identifying key challenges for clinical deployment.