1. Mammogram Data Analysis: Trends, Challenges, and Future Directions 2. AI in Breast Imaging: Applications, Challenges and Future Research 3. Prediction of Breast Cancer Diagnosis Using a Random Forest Classifier 4. Medical Image Analysis of masses in Mammography using Deep Learning model for Earlier Diagnosis of Cancer Tissues 5. A framwork for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning 6. Autoencoder based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures 7. Prognosis of breast cancer using machine learning classifiers 8. Breast cancer diagnosis through microcalcification 9. Scutinization of Mammogram Images using deep learning 10. Computational Techniques for Analysis of Breast Cancer Using Molecular Breast Imaging 11. Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging 12. Efficient Transfer Learning Techniques for Breast Cancer Histopathological Image Classification 13. Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods 14. An automatic level set segmentation of breast Tumor from mammogram images using optimized Fuzzy c-means clustering

Presents novel ideas for AI based mammogram data analysis Discusses the roles deep learning and machine learning techniques play in efficient processing of mammogram images and in the accurate defining of different types of breast cancer Features dozens of real-world case studies from contributors across the globe