# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology
### Transfer Learning Models  
A wide range of transfer learning techniques was used to improve the suggested breast cancer classification system's performance and capacity for generalization. Since transfer learning allows information obtained from large-scale datasets like ImageNet to be reused, it has emerged as a key strategy in medical image analysis, especially when working with sparse datasets. Several trained models, including MobileNet, Xception, DenseNet201, VGG16, ResNet50, and EfficientNetB0, as well as specially created CNNs, are among the tactics used. Each model was fine-tuned with custom dense layers for binary classification (benign vs. malignant), offering unique structural advantages in feature extraction and computational efficiency. * MobileNet inspired a bespoke CNN that uses depth-wise separable convolutions to lower the number of parameters and computational cost without sacrificing speed. The four separate convolution blocks in this lightweight design are followed by batch normalizing and max pooling, which results in dense layers that are completely linked and include dropout regularization. It serves as a foundational architecture for comparison against more complex pre-trained models;
* MobileNet, a highly efficient network designed for mobile and embedded vision applications, further leverages depth-wise separable convolutions and a streamlined structure. In this work, the base MobileNet was frozen and extended with dense layers (1024, 512) and dropout to prevent overfitting. Its main advantage lies in its low latency and memory footprint while achieving strong performance on small medical datasets;
* The Xception model, built upon depth-wise separable convolutions and residual connections, expands on Inception modules by replacing them with extreme Inception blocks. In this approach, a stack of thick layers (2024, 1024, 512) with dropout was added for binary classification, while the ImageNet-trained layers were frozen. The strength of Xception is its ability to preserve parameter efficiency while capturing fine-grained spatial hierarchies in medical pictures;
* In order to encourage feature reuse and mitigate the vanishing gradient issue, DenseNet201, which is renowned for its dense connection across layers, was also used. DenseNet promotes gradient flow and increases representational power by  
Figure 8: Class Distribution in the Fusion Dataset. feed-forwardly linking each layer to every other layer. Here, it was augmented with dense layers (2024 \(\times\) 2, 1024) and dropout layers to tailor the model for binary decision-making in cancer detection;
* VGG16 was employed for its simplicity and consistency. VGG16's consistent architecture of 3 \(\times\) 3 convolution layers and 2 \(\times\) 2 pooling layers makes it resilient and interpretable even if it lacks the sophisticated advances of more current models. In this study, VGG16's convolutional base was frozen, and new dense layers (2048, 1024, 512) were stacked to adapt it for the classification task. The key strength of VGG16 lies in its deep hierarchical structure, which is particularly beneficial for capturing visual patterns in high-resolution mammographic and ultrasound images;
* ResNet50, another widely used model, addresses the degradation problem in very deep networks through the introduction of residual learning. The residual blocks al low gradients to flow directly through skip connections, enabling the training of deeper architectures. In this implementation, the ResNet50 model was extended with multiple dense layers (2024, 1024, 512, 256, 128) and dropout, providing a deep and expressive architecture that balances depth and training stability;
* The method was modified to use EfficientNetB0, a more modern architecture that scales depth, breadth, and resolution in a compound way. It is renowned for using fewer FLOPs and parameters to achieve excellent precision. EfficientNet's efficient scaling strategy allows it to outperform traditional CNNs on various benchmarks.