An enhanced hybrid model paradigm for transforming breast cancer prediction

Breast cancer is the most common cancer among women worldwide, underlining the significance of early identification for successful treatment. Deep learning (DL) has shown promising results in breast cancer prediction, but traditional DL models struggle with imbalanced datasets making the model biased. To overcome this problems hybrid models are built but, these hybrid models often consume huge resources making them computationally inefficient. This study introduces an innovative breast cancer classification framework using deep convolutional neural networks. The framework relies on weight factors and threshold values to create an effective hybrid model. Initially, two separate deep convolutional neural network models are employed, and their test accuracies are compared to a predefined threshold. If both models accuracy falls below the threshold, a hybrid model is constructed. This hybrid model merges features from both models through a multimodal fusion approach, expanding the feature set but potentially affecting computational efficiency. To address this efficiency challenge, an optimal feature selection algorithm is employed to choose the most relevant features from the expanded set. Empirical evidence validates the framework’s excellence, even when dealing with imbalanced datasets, as it surpasses evaluation criteria. The suggested hybrid model achieves an impressive binary classification accuracy of 99.69% while maintaining a minimal processing time of just 3.52 seconds.