# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 2 Related Work
### Analysis of Existing Attention Mechanisms for Skin Cancer Detection  
In skin cancer detection, attention mechanisms play a crucial role in enhancing the performance of CNNs by directing the focus of the model to the most relevant regions of dermatoscopic images. Self-attention mechanisms: Self-attention mechanisms, such as those employed in transformers, compute the attention scores across all positions in the input image, enabling the model to assess the importance of each pixel relative to every other. This capability allows self-attention mechanisms to capture relationships between distant pixels, which is particularly beneficial for identifying subtle patterns spanning a lesion. Their flexibility allows them to adapt to various image features, thereby enhancing the generalizability of the model. However, self-attention mechanisms are computationally intensive and require a substantial amount of memory, particularly for processing high-resolution images. In addition, their increased complexity makes it more challenging to train and fine-tune models. Although highly effective at capturing intricate patterns, the significant computational overhead limits their practicality for real-time diagnostic systems. Spatial attention mechanisms: These systems focus on identifying important spatial regions in images. They typically generate a spatial attention map that highlights the areas of interest, which helps the model focus on critical regions such as the boundaries and texture variations of the lesions. They are generally less computationally demanding than self-attention mechanisms, however, they may not capture the long-range dependencies as effectively as the self-attention mechanisms. There is a risk that the model may overemphasize certain regions while neglecting others that may also be relevant. They are particularly useful for enhancing the focus of the model on lesion characteristics and improving diagnostic accuracy. Channel attention mechanisms: These systems focus on the importance of different feature channels, allowing the model to weigh the channels that carry more relevant information. They enhance important features (e.g., color and texture) that are indicative of malignancy and can be easily integrated into existing CNN architectures. Although this is effective in emphasizing certain features, it may not adequately address the spatial localization. These systems can improve the sensitivity of the model to specific diagnostic features, thereby aiding in differentiating between benign and malignant lesions. Dual attention mechanisms: This type of mechanism combines both spatial and channel attention, enabling the model to simultaneously focus on significant regions and features. It balances spatial and feature importance, providing a holistic approach to image analysis. This leads to an improved overall performance by leveraging the strengths of both spatial and channel attention. Combining these two attention mechanisms can increase the complexity and computational requirements of the model. This mechanism offers a balanced approach, making it highly effective for the detailed and accurate detection of skin cancer. Hierarchical attention mechanisms: These systems apply attention at multiple levels of the network, progressively refining the focus. They gradually narrow the attention to the most critical features and regions through multiple layers. This facilitates a detailed and nuanced image analysis, and these systems are complex and challenging to train effectively, requiring additional computational resources. It is beneficial to progressively focus on the relevant details, leading to a more precise diagnosis. Each attention mechanism offers distinct advantages and has specific limitations in skin cancer detection. In the proposed DCAN-Net framework, a systematic evaluation of these mechanisms ensures the selection of the most effective approach, thereby enhancing the diagnostic accuracy and reliability of the model for skin cancer detection.