Transfer learning with class-weighted and focal loss function for
  automatic skin cancer classification
  Skin cancer is by far in top-3 of the world's most common cancer. Among
different skin cancer types, melanoma is particularly dangerous because of its
ability to metastasize. Early detection is the key to success in skin cancer
treatment. However, skin cancer diagnosis is still a challenge, even for
experienced dermatologists, due to strong resemblances between benign and
malignant lesions. To aid dermatologists in skin cancer diagnosis, we developed
a deep learning system that can effectively and automatically classify skin
lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell
Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6)
Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train the
system. An end-to-end deep learning process, transfer learning technique,
utilizing multiple pre-trained models, combining with class-weighted and focal
loss were applied for the classification process. The result was that our
ensemble of modified ResNet50 models can classify skin lesions into one of the
seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%,
respectively. This deep learning system can potentially be integrated into
computer-aided diagnosis systems that support dermatologists in skin cancer
diagnosis.
