# Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection
## 2 Literature Review  
CNN architectures have demonstrated significant capabilities in analyzing spatio-temporal patterns, including in the domain of skin disease recognition [16; 17], thanks to the accumulation of information over time through the collective computations of each convolutional neuron. In [18], the authors employed two multimodal datasets consisting of photos illustrating skin problems and metadata pertaining to clinical trials. The model they implemented consisted of a decoder and two encoders, which were influenced by the transformer architecture. Multiple Vision Transformers (ViTs) were compared to several commonly used CNN designs. Among them, NesT achieved a better accuracy rate of 75% compared to DenseNet121 and ResNet101, which reached 66% accuracy. NesT served as the network's support structure during the analysis of the private dataset. In this situation, the SLE and MA block methods were found to be more appropriate for the metadata than the one hot encoder technique. When the suggested model was compared to the alternative model that accepts input photos, the accuracy increased from 0.75 to 0.816. It is crucial to acknowledge that image processing and segmentation have a significant function in the medical field. The melanoma parameters can be utilized for image segmentation and feature extraction phases to detect melanoma, a kind of skin cancer. The image is classified as either cancer-free or as a melanoma cancer lesion based on the texture, size, and form obtained from feature parameters. The authors of [19] proposed that computer vision may help diagnose images in healthcare. Computerized diagnostic methods are needed to diagnose melanoma early and solve the problem. Segmenting skin lesions is the initial step. Performing feature and pattern analysis is crucial to diagnosing the afflicted area. Combining CNN structures with multi-scale data has led to many skin lesion segmentation methods. These systems either require extra labeling or use cumbersome, ineffective components. Researchers combined ViT and ConvNet to create a hybrid segmentation method. This method pinpointed infections and skin lesions with great precision. To classify skin abnormalities, numerous researchers opt to carry out comprehensive studies on the readily accessible skin cancer dataset known as Human Against Machine (HAM10000) [20]. In [21], a basic transformer framework was used to classify skin cancer using this dataset and a clinical dataset. The suggested model outperformed state-of-the-art techniques on the HAM10000 dataset, achieving 94.3% accuracy. The contrastive learning-based multi-scaled network outperformed CNN models, such as MobileNetV2, ResNet50, and InceptionV2, with 94.1% accuracy. In [22], the authors created a two-tier approach to handle skin disease classification issues in separate studies. Their MVT model was thoroughly tested on the HAM10000 dataset to classify SC images. The training set had 98% accuracy, and the validation set had 90% accuracy without data preprocessing. The authors trained the same dataset with the GrabCut-stacked CNN (GC-SCNN) model with a fuzzy foundation. SVM and fuzzy GC-SCNN achieved 99.75% classification accuracy in this case. The authors of [23] asserted that pathologists interpret skin biopsy specimens by using visual judgments, which can frequently be prone to inaccuracies. A Scale-Aware Transformer Network (ScAtNet) was used to categorize melanocytic skin lesions in digital Whole-Slide Images (WSIs) to resolve this issue. To accomplish this, CNN was employed to independently create mappings for each individual data scale in a patch-wise manner. Subsequently, the model utilized a transformer to acquire inter-scale and inner patch representation from the combined multi-scale patch embeddings. The network was evaluated against patch-based classification, ChikonMIL, weighted feature aggregation, a streaming CNN, and MS-DA-MIL. The test set attained an accuracy of 64% across various input scales.