<p>Accurately identifying axillary lymph node involvement is essential for proper staging and treatment planning in breast cancer. In this work, we introduce a deep learning framework, ConvNeXt-Based Prognostication of Axillary Lymph Node Involvement in Breast Cancer via Smart CT Interpretation, designed to improve diagnostic precision. The approach begins with advanced CT image preprocessing including intensity normalization, noise reduction using non-local means filtering, and adaptive histogram equalization to better highlight important anatomical features while minimizing artifacts. At the heart of our method is the ConvNeXt architecture, a modern evolution of convolutional networks inspired by ideas from transformers. By using large-kernel depth wise convolutions, layer normalization, and inverted bottlenecks, ConvNeXt is able to capture both detailed local patterns and broader contextual information across CT volumes. These capabilities make it particularly effective at detecting subtle signs of lymph node metastasis, such as cortical thickening or the loss of fatty hilum. With its stage-wise feature scaling and hierarchical feature aggregation, ConvNeXt enables deeper, multi-scale interpretation of CT images. Combined with a smart CT analysis pipeline, the model uncovers hidden radiological patterns that are often missed by manual review. Experimental results show significant improvements over conventional CNNs, highlighting the framework’s potential for advancing non-invasive breast cancer prognosis.</p>