# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
## 2 Related Work
### Image Classification  
An article published [23] in the BMC Health Services Research journal states that skin cancer is a substantial public health problem in South Africa due to the high levels of sunlight [24]. Thus, the need for innovative new ways to aid in the detection of skin cancer in Africa and similar geographies is paramount. This is emphasised in an article published in the National Library of Medicine [23] which calls for the development of novel and non-invasive methods of diagnosing skin cancer. Image classification could provide a non-invasive and scalable way to classify skin lesions. To this end, we investigated related works that used CNNs trained on the HAM10000 (Human Against Machine with 10,000 training images') dataset [25] to create skin lesion image classification systems.  
[PERSON] et al. [26] developed a'skin cancer identification system' in 2019. Their paper does not explicitly mention what hardware was used to train their model; however, it was found that their system produced a training accuracy of 80% and a test accuracy of 78%. [PERSON] et al. [27] developed a Deep CNN (DCNN) for skin cancer classification in 2020. They also do not explicitly state what hardware was used to train their model; however, it was found that their model produced very high training and test accuracies of 99%. Their high accuracy is most likely due to a combination of training over 100 epochs and the specific hyperparameters chosen. A more detailed analysis of the model hyperparameters and results can be found in Section 5.  
Further noteworthy research in this field includes [PERSON] et al. [28], who developed a Raspberry Pi-based application analysing a database of skin lesion images of three neglected tropical diseases regardless of the epidemiological and clinical background of patients, including leprosy, Buruli ulcer, and leishmaniasis. Their approach, based on hybrid whale optimization and shark smell optimization (WOA-SSO), reached an accuracy of 93%. [PERSON] et al. [29] proposed 'UCM-Net', a model for skin lesion segmentation. Its main purpose is to achieve high performance with a minimal parameter count, making it suitable for real-time medical imaging. The model combines multi-layer perceptron (MLP) and CNN architectures, which performed well (a mean IoU (Intersection over Union, an evaluation metric used to determine the accuracy of an object detection or segmentation model on a particular dataset) score of 81%) when tested on the ISIC 2017 dataset [30]. [PERSON] et al. [31] discussed the Double-Condensing Attention Condenser (DC-AC) architecture and its applications in skin cancer detection. They found that the DC-AC architecture performed well in terms of computational performance compared to related works. However, its accuracy was lower than that of related works--which is problematic for a skin cancer detection system. Finally, [PERSON] et al. [32] raised an interesting point about how the detection of malignant melanoma and other skin lesions is highly dependent on where the lesion is on the body. They developed a deep learning framework that classifies skin lesions into three main anatomical sites: (1) face, (2) trunk and extremities, and (3) acral. Their framework achieved an accuracy of 91% using the EfficientNetB0 architecture [33].