## 3 Materials and Methods
### Presented CAD
#### 3.3.2 Development and Training of Compact CNNs  
This study employs transfer learning deploying three lightweight CNNs that have been pre-trained on the data set known as ImageNet. MobileNet, EfficientNetB0, and ShuffleNet were selected as the primary CNN architectures for feature extraction in the proposed CAD system due to several factors, such as their performance in resource-constrained scenarios, computational efficiency, and compatibility with transfer learning. Each of these deep neural networks possesses distinct advantages, rendering them appropriate for diverse deep learning applications. ShuffleNet employs pointwise group convolution and channel shuffle processes to markedly decrease the computational burden while maintaining the effective extracting of features, rendering it exceptionally appropriate for mobile and embedded systems [33]. MobileNet leverages depthwise separable convolutions to markedly decrease model complexity and computational demands, attaining high efficiency while maintaining accuracy [31]. EfficientNetB0 employs a hybrid scaling technique to optimize network depth, width, and resolution, attaining exceptional accuracy while preserving computational efficiency [32]. Collectively, these architectures provide ver  
Figure 2: Summary of the steps of the presented CAD system. satility, accommodating both resource-limited applications and tasks necessitating high performance, demonstrating their adaptability across diverse real-world contexts. Though other lightweight architectures, such as GhostNet and RegNet, had their own merits, the selected models seemed to match the specific requirements in thermographic breast cancer detection. The architectures were selected because they are proven to perform with a trade-off between accuracy and computational efficiency, making them suitable in real-world clinical deployments where high-performance hardware may not always be available. While GhostNet decreases redundancy through its innovative ghost modules, low reliance on linearity may not capture the non-linear thermal patterns inherent in breast thermography. Complex heat variations observing breast cancer thermograms themselves are features requiring powerful non-linear modelling. Therefore, these intricate thermal patterns are better represented by MobileNet and ShuffleNet, characterised by non-linear activation layers (e.g., ReLU6)-thereby ensuring a more accurate representation of the underlying features, thus subdividing into labor with each class. RegNet's merit lies in its systematic architecture search for generic tasks, giving flexibility and scalability across domains. However, since RegNet has larger parameter counts (for example, RegNetY-4 GF has 21M parameters), it does not serve the purpose of lightweight deployment in resource-constrained clinical environments. Instead, EfficientNetB0 compounds its scaling methods such that it strikes a more optimized balance of model depth, width, and resolution for maximum performance toward medical imaging tasks without excessive compute overhead. Hence, EfficientNetB0 would be a proper fit since it gives preference to accuracy while being efficient. Besides, the chosen architectures (MobileNet, EfficientNetB0, and ShuffleNet) are some of the most commonly used in transfer learning applications, thanks to their pre-trained weights on large-scale datasets (such as ImageNet). This allows the models to generalize well to new domains without too much fine-tuning and thus reduce the reliance on a lot of labeled data. In the case of breast cancer thermography, where labeled datasets are usually scarce, transfer learning is crucial in achieving high diagnostic accuracy. Even though GhostNet and RegNet also allow this, they do not have a fairly established record compared to MobileNet, EfficientNetB0, and ShuffleNet. All chosen architectures offer various advantages in terms of feature representation and layer-wise learning, which is very valuable to identify the minute thermal anomalies presenting with breast cancer.