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Skin cancer is by far in top-3 of the world's most common cancer. Amongdifferent skin cancer types, melanoma is particularly dangerous because of itsability to metastasize. Early detection is the key to success in skin cancertreatment. However, skin cancer diagnosis is still a challenge, even forexperienced dermatologists, due to strong resemblances between benign andmalignant lesions. To aid dermatologists in skin cancer diagnosis, we developeda deep learning system that can effectively and automatically classify skinlesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal CellCarcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6)Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train thesystem. An end-to-end deep learning process, transfer learning technique,utilizing multiple pre-trained models, combining with class-weighted and focalloss were applied for the classification process. The result was that ourensemble of modified ResNet50 models can classify skin lesions into one of theseven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%,respectively. This deep learning system can potentially be integrated intocomputer-aided diagnosis systems that support dermatologists in skin cancerdiagnosis.

Transfer learning with class-weighted and focal loss function for automatic skin cancer classification