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Transfer learning with class-weighted and focal loss function for automatic skin cancer classification.
TLDR
A deep learning system that can effectively and automatically classify skin lesions into one of the seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%, respectively is developed and can potentially be integrated into computer-aided diagnosis systems that support dermatologists in skin cancer diagnosis.Abstract:
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.read more
Citations
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Journal ArticleDOI
An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models
TL;DR: A deep convolutional neural network model based on deep learning approach for the accurate classification between benign and malignant skin lesions is proposed and defined as more reliable and robust when compared with existing transfer learning models.
Journal ArticleDOI
Analysis of the ISIC image datasets: Usage, benchmarks and recommendations.
TL;DR: The International Skin Imaging Collaboration (ISIC) dataset has become a leading repository for researchers in machine learning for medical image analysis, especially in the field of skin cancer detection and malignancy assessment as discussed by the authors.
Journal ArticleDOI
Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning
TL;DR: The designed CNN model showed results comparable to the pretrained model andSimulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful, and an 83.2% accuracy rate was achieved.
References
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Journal ArticleDOI
Focal Loss for Dense Object Detection
TL;DR: Focal loss as discussed by the authors focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training, which improves the accuracy of one-stage detectors.