  While skin cancer detection has been a valuable deep learning application for
years, its evaluation has often neglected the context in which testing images
are assessed. Traditional melanoma classifiers assume that their testing
environments are comparable to the structured images they are trained on. This
paper challenges this notion and argues that mole size, a critical attribute in
professional dermatology, can be misleading in automated melanoma detection.
While malignant melanomas tend to be larger than benign melanomas, relying
solely on size can be unreliable and even harmful when contextual scaling of
images is not possible. To address this issue, this implementation proposes a
custom model that performs various data augmentation procedures to prevent
overfitting to incorrect parameters and simulate real-world usage of melanoma
detection applications. Multiple custom models employing different forms of
data augmentation are implemented to highlight the most significant features of
mole classifiers. These implementations emphasize the importance of considering
user unpredictability when deploying such applications. The caution required
when manually modifying data is acknowledged, as it can result in data loss and
biased conclusions. Additionally, the significance of data augmentation in both
the dermatology and deep learning communities is considered.
