Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. According to the World Health Organization, early detection and accurate diagnosis play a crucial role in reducing mortality rates and improving treatment outcomes. Despite advancements in diagnostic technologies, manual analysis of mammogram images is time-consuming, prone to variability, and requires expert radiological interpretation. As a response to these challenges, this study proposes an innovative and efficient hybrid machine learning framework that combines the deep learning capabilities of Convolutional Neural Networks (CNNs) with the classification strength of Support Vector Machines (SVMs) for breast cancer detection and classification from mammographic images. The model leverages the CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset—a well-established benchmark for mammographic image analysis. The images undergo a series of pre-processing steps including greyscale normalization, contrast enhancement, resizing, and noise reduction, all of which aim to ensure consistent quality and effective feature learning. A custom CNN architecture is then designed to extract high-level features from the pre-processed images. This network is optimized for capturing complex patterns such as masses, calcifications, and tissue asymmetries commonly observed in breast cancer cases. Unlike conventional end-to-end CNN classification, this study uses the CNN primarily for deep feature extraction .The extracted features are subsequently passed to an SVM classifier, which constructs a decision boundary to accurately separate benign from malignant cases. This hybrid model addresses several challenges inherent to medical image analysis: it mitigates the risks of over fitting associated with deep learning models trained on limited data and improves classification performance on imbalanced datasets through the SVM’s generalization capability. The proposed hybrid CNN-SVM model achieves a classification accuracy of 91.7%, with competitive precision, recall, and F1- scores, highlighting its potential effectiveness in real-world clinical scenarios. This study’s contributions are multifold: the development of a novel hybrid classification framework, the successful application of deep learning techniques for mammographic image analysis, and the demonstration of improved diagnostic accuracy through AI-driven methods. The research underscores the importance of interdisciplinary approaches combining medical imaging, artificial intelligence, and statistical learning for advancing cancer diagnostics. In future work, the integration of transfer learning, explainable AI, and real-time decision support systems could further enhance the diagnostic reliability and acceptance of such tools in clinical environments. The findings of this study pave the way for future advancements in computer-aided diagnosis systems and support the global effort to combat breast cancer through technology
K. Swathi (Mon,) studied this question.