Introduction Detection and classification of skin lesions such as melanoma and basal cell carcinoma is a growing field within dermatology studies. The use of deep learning models such as convolutional neural networks can be incredibly accurate in identifying such cancers. However, due to the skin cancer prevalence in those of Caucasian backgrounds, the majority of these deep learning models train on data skewed toward lighter skinned images. This inevitably leads to underlying bias in the model’s ability to detect and classify skin cancers of people with darker skin. Thus, our aim is to utilize deep learning for correcting model bias. By using a style transfer model architecture combining two different convolutional neural networks, we are able to take darker skin images and images of skin lesions to produce images of darker skin lesions that could be used for model training.
Challenges The main challenge we are facing is the training process in choosing correct hyperparameters. Currently our model is producing a maximum test accuracy of around 40%. We had issues with overfitting, finding the correct learning rate, and figuring out how to save weights from the loss function at each layer. We are not exactly sure how to improve off of this model accuracy, but we are continuing to make improvements. The performance of this classification model has a large impact on the performance of the style transfer model.
Insights We have been able to produce an initial image from the style transfer. However, the resulting images are not as good as we hoped. For example, some important features (i.e., skin lesions) were developing on the image, but as the image adjusted to style, unrealistic colors began appearing. This may be because the raw loss for the stylized images is much smaller than the raw loss for the featured images which can cause adjusting the weights between these losses difficult. Further, it is possible that our CNN model is not identifying features as well as we hoped, causing the model to stylize the image poorly.
Plan We are on track with our plan. We hope to continue modifying our hyperparameters and minor model architecture layouts in hope to improve the training and testing accuracy of the CNN model. We are also currently working on the style transfer element which requires tuning of our loss functions to ensure a good balance of style and important features.
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