Enhancing Breast Cancer Classification: A Few-Shot Meta-Learning Framework with DenseNet-121 for Improved Diagnosis

Breast cancer is a significant health concern globally, requiring early and accurate detection to improve patient outcomes. However, manual detection of breast cancer from medical images is time-consuming and inaccurate. Accurate assessment of cancer stages is critical for effective treatment and post-diagnosis handling. The goal of this research is to develop a specialized meta-learning method for classifying breast cancer images, particularly effective when working with limited data. Traditional cancer stage classification methods often struggle with insufficient labeled data, but meta-learning addresses this challenge by rapidly adapting to new tasks with few examples. The proposed method begins with image segmentation to identify regions of interest in the medical images, followed by thorough feature extraction to capture essential data representations. The critical meta-training phase involves refining a classifier within a metric space, utilizing cosine distance and an adaptable scale parameter. During the meta-testing stage, the adapted classifier predicts cancer stages using minimal support samples, achieving approximately 96% accuracy. This approach shows significant promise for the medical field, providing practical solutions to enhance diagnostic processes and improve predictions for breast cancer detection and treatment.