Clinical Report: Integrative Approaches for Breast Cancer Diagnosis

Background

Breast cancer remains a leading health issue globally, with early and accurate diagnosis being crucial for improving patient outcomes. Traditional imaging modalities have limitations that can hinder effective diagnosis. The integration of artificial intelligence, particularly deep learning, offers advancements in analyzing medical images for detection and decision-making.

Data Highlights

Key Findings

• The proposed model utilizes attention-based transformers for feature extraction.
• Modified mantissa search (MMS) algorithm effectively removes irrelevant features.
• American zebra optimization (AZO) algorithm enhances feature integration with missing data.
• Lightweight convolutional neural network (LCNN) is employed for classification to maintain diagnostic accuracy.
• The model achieved accuracy rates of 98.958%, 97.37%, and 99.438% on MIAS, BreakHis, and combined multimodal datasets, respectively.

Clinical Implications

The multimodal approach integrates various imaging modalities, which may enhance diagnostic accuracy in breast cancer.

Conclusion

The integration of advanced deep learning techniques in breast cancer diagnosis requires further validation in clinical practice.

Related Resources & Content

1. DIGITAL HEALTH, 2026 -- Breast lesion identification using feature fusion and multiresolution dual-tree complex wavelet transform
2. Frontiers in Oncology, 2026 -- Multimodal feature fusion model for breast mass malignant risk stratification
3. Frontiers in Oncology, 2026 -- Multimodal data fusion: integrating PET/MRI and liquid biopsy for a holistic view of cancer biology
4. Frontiers in Digital Health, 2026 -- Explainable AI in breast cancer ultrasound imaging: current developments and challenges
5. Recommendation: Breast Cancer: Screening | United States Preventive Services Taskforce
6. Clinical and Experimental Medicine, 2025 -- Performance of digital breast tomosynthesis with digital mammography for detecting breast cancer in the diagnostic setting: a meta-analysis
7. ScienceDirect, 2024 -- Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI)
8. Recommendation: Breast Cancer: Screening | United States Preventive Services Taskforce
9. Performance of digital breast tomosynthesis with digital mammography for detecting breast cancer in the diagnostic setting: a meta-analysis | Clinical and Experimental Medicine | Springer Nature Link
10. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study - ScienceDirect