Abstract
Skin cancer malignant melanoma is the deadliest type of cancer and early detection is important to improve patient prognosis.
Recently, Deep Learning Neural Networks (DLNNs) have proven to be a powerful tool in classifying medical images for detecting
various diseases and it has become viable to deal with skin cancer detection. In this research we propose a serverless mobile app to assist
with skin cancer detection. This mobile app is based on the best performance of five Convolution Neural Network (CNN) models designed
from scratch as well as four state-of-the-art architectures used for Transfer Learning (Inception v3, ResNet50v2, DenseNet, and Exception
v2). Since the skin cancer dataset is imbalanced, we perform data augmentation. We also use the fine-tuning top layers technique for
feature extraction on all models to improve the results. The main novelty of the proposed method is the model deployed as part of mobile
app where the classification processes are executed locally on the mobile device. This approach will reduce the latency and improve the
privacy of the end users compared with the cloud-based model where user needs to send images to a third-party cloud service. The
achieved accuracy of pre-trained Inception v3 model is 99.99%. Therefore, the proposed mobile solution can serve as a reliable tool that
can be used for melanoma detection by dermatologists and individual users.