# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 2 Related Work
### Deep Learning Methods  
Deep CNNs show potential for skin cancer classification and overcome the limitations of conventional machine learning methods. For instance, [PERSON] et al. [22] presented a CNN-based model that was trained on 129,450 clinical images and achieved a promising performance comparable to dermatologists in two binary classification use cases. This artificial intelligence could extend the dermatologists' reach beyond clinics, as 6.3 billion smartphone subscriptions are projected to exist by 2021. To differentiate between melanoma and benign cases, [PERSON] et al. [23] employed clinical image-based methods that were preprocessed to reduce light and noise effects. Their method performed better than the other existing approaches in terms of diagnostic accuracy, making it a helpful tool for dermatologists in the early diagnosis of melanoma. For instance, a deep belief network and self-advised SVM were utilized in a self-supervised model for melanoma diagnosis. To increase generalization and decrease redundancy, a bootstrapping technique was adopted. Studies revealed that this approach performed better than other systems including KNN and SVM. For melanoma identification, a straightforward CNN can be used to preprocess images to remove noise. [PERSON] et al. [24] suggested that a good visual examination model is essential for correctly classifying and detecting diseases. The creation and testing of algorithms have enhanced image-order systems with the availability of multiple open image datasets. In a recently developed database for skin cancer, CNNs outperformed other established AI technologies. This study analyzed skin cancer images from the International Skin Imaging Collaboration (ISIC) dataset to classify the images into harmful or compassionate classifications using a prebuilt CNN. The highest accuracy was achieved using 128 \(\times\) 128 images (83.78% accuracy). For accurate skin lesion classification, [PERSON] et al. [25] proposed a real-time mobile-enabled skin lesion classification expert system, \"i-Rash\", using CNN and cloud-server architecture. The system aims to diagnose acne, eczema, and psoriasis in remote locations. The model used SqueezeNet, a transfer learning approach, and was trained on 1856 images. The system achieved an accuracy, sensitivity, and specificity of 97.21%, 94.42%, and 98.14%, respectively. [PERSON] et al. [26] employed a 16-layer CCN model to detect and classify cancers based on clinical images and obtained promising results, maintaining the false-negative rates below 10% and achieving a precision above 80%. [PERSON] et al. [27] proposed the VGG-16-based model for effective skin cancer detection. They utilized the ISIC dataset with 2460 colored images and obtained an accuracy of 87.6%, demonstrating the significant outcome of CNN in skin cancer detection.  
[PERSON] and [PERSON] [28] proposed a computer-aided diagnosis (CAD) system to categorize medical images by adopting a transfer learning strategy in a MobileNet pretrained model using the ISIC dataset. Their model achieved promising performance compared with other existing methods. [PERSON] and [PERSON] [29] built a Mask R-CNN model for lesion segmentation using pre-trained weights from the Microsoft COCO dataset. Experiments on benchmark ISIC 2018 datasets demonstrated 96% accuracy in lesion boundary segmentation and 80% balanced multiclass accuracy. [PERSON] et al. [30] also evaluated the system, as skin cancer is a major health concern with 125,000 new melanoma cases annually. Early detection can decrease mortality rates. A multiclass skin cancer detection system using a pretrained MobileNet model and the HAM10000 ISIC dataset achieved high categorical accuracies of 80.81%, 91.25%, and 96.26%, respectively, making it a fast and expensive method for clinical advancement.