What question did this study set out to answer?

The aim is to summarize advancements in imaging techniques for the early detection of breast cancer and evaluate the effectiveness of classical versus deep learning methods.

February 14, 2026Open Access

Advances in Medical Image Processing for Early Breast Cancer Detection: Classical Techniques and Deep Learning Perspectives

Key Points

The aim is to summarize advancements in imaging techniques for the early detection of breast cancer and evaluate the effectiveness of classical versus deep learning methods.
Reviewed classical imaging techniques including mammography, ultrasound, and MRI.
Analyzed preprocessing steps such as denoising, deblurring, and contrast enhancement.
Compared traditional methods like thresholding and edge detection with deep learning models.
Evaluated performance based on AUC and accuracy metrics across various studies.
Deep learning models significantly outperformed classical methods with AUC over 0.85 to 0.95.
Representative studies showed gains of 5-15% in AUC/accuracy when using AI techniques.
Challenges in clinical translation include interpretability and dataset constraints.

Abstract

Breast cancer is the most common malignancy among women and a leading cause of cancer-related mortality, making early and accurate detection essential. This review summarises advances in breast imaging and computational diagnostics across mammography, ultrasound, and magnetic resonance imaging (MRI), highlighting challenges in differentiating benign from malignant lesions and identifying rarer tumour types. Key preprocessing steps—denoising, deblurring, and contrast enhancement—are reviewed as they improve image quality prior to analysis. Classical methods (e.g., thresholding, edge detection, and region growing) are compared with deep learning approaches for segmentation and classification. CNNs, RNNs, and emerging transformer-based models consistently outperform handcrafted pipelines, with representative studies reporting 5–15% gains in AUC/accuracy and deep models achieving AUC > 0.85–0.95 on several benchmarks. The review also discusses dataset constraints, common evaluation metrics (AUC, Dice, sensitivity, specificity), and clinical translation barriers such as interpretability and domain shift. Overall, AI-driven methods show strong potential to enhance early detection and support improved breast cancer outcomes.

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Cite This Study

Jin et al. (Thu,) studied this question.

synapsesocial.com/papers/699011602ccff479cfe57fd9 https://doi.org/https://doi.org/10.3390/electronics15040790

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