# Smart Embedded System for Skin Cancer Classification
## 2 Background and Related Work
### Skin Cancer Detection Using Deep Learning  
The most common type of cancer is skin cancer, and it is spreading [21]. Detecting skin cancer at an early stage increases the survival rate to around 97% [22]. The medical procedure for skin cancer detection is time-consuming and aggressive to the patient when lesions must be biopsied [21].  
Recently, AI-based algorithms have brought significant contributions to medical diagnosis; in particular, those based on medical imaging. Recent techniques, such as dermoscopy, have improved the visualization of lesions, improving the accuracy with which dermatological diagnosis are made. Many applications based on deep learning are already used to detect breast cancer, lung cancer, skin lesions, etc.  
A dermatologist follows a sequence of steps to diagnose skin cancer, from direct observation to a biopsy. However, this is a time-consuming process that compromises an early cancer detection [23].  
Image classification with deep learning has therefore been applied to help the medical diagnosis. [PERSON] et al. [24] considered several CNN-based models to classify images in the HAM10000 dataset. Each individual output is combined into a decision fusion module. The results report an accuracy of 86.71%, 3% higher than the best individual model. The better accuracy has a high cost in terms of computing resources and energy and, therefore, is not appropriate for portable devices. [PERSON] et al. [25] proposed a deep-learning model as a combination of MobileNet and a long short-term memory model. The combined model achieved an accuracy of 85% on the HAM10000 dataset.  
In [26], [PERSON] et al. considered a mask recurrent neural network and a pyramid neural network to classify images from the HAM10000 dataset, with a precision of 87%. The work was improved to an accuracy of 90.7% [27] using a color-controlled histogram, a new saliency segmentation technique with a CNN. Finally, a kernel extreme learning machine classifier was used.  
[PERSON] and [PERSON] [28] also considered the skin cancer identification problem. The solution applies transfer learning to a CNN, obtaining a validation accuracy of 87.8%. [PERSON] et al. [29] used the MobileNet model and transfer learning to classify HAM10000 images, with a final accuracy of 83.1%. [PERSON] et al. [30] and [PERSON] et al. [31] also considered deep learning and transfer learning for skin cancer classification. The second only distinguished between melanoma and non-melanoma images, instead of classifying among the seven possible classes of HAM10000.  
In general, deep neural models and transfer learning are used to train models for skin cancer classification. Most solutions have an accuracy of around 87%, with lower-complexity models such as MobileNet achieving 85% and more complex models achieving almost 91%.  
In this work, the family of ResNet models was considered and explored in order to implement the classification problem with incremental inference. However, other models could be considered, such as MobileNet. The proposed incremental inference can also be run with different models. For example, a MobileNet could be used for the lowest complexity model and a ResNet for the more accurate model.