Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate.,Early detection and resection are two key points for a successful cure.,Recent researches have used artificial intelligence to classify melanoma and nevus and to compare the assessment of these algorithms to that of dermatologists.,However, training neural networks on an imbalanced dataset leads to imbalanced performance, the specificity is very high but the sensitivity is very low.,This study proposes a method for improving melanoma prediction on an imbalanced dataset by reconstructed appropriate CNN architecture and optimized algorithms.,The contributions involve three key features as custom loss function, custom mini-batch logic, and reformed fully connected layers.,In the experiment, the training dataset is kept up to date including 17,302 images of melanoma and nevus which is the largest dataset by far.,The model performance is compared to that of 157 dermatologists from 12 university hospitals in Germany based on the same dataset.,The experimental results prove that our proposed approach outperforms all 157 dermatologists and achieves higher performance than the state-of-the-art approach with area under the curve of 94.4%, sensitivity of 85.0%, and specificity of 95.0%.,Moreover, using the best threshold shows the most balanced measure compare to other researches, and is promisingly application to medical diagnosis, with sensitivity of 90.0% and specificity of 93.8%.,To foster further research and allow for replicability, we made the source code and data splits of all our experiments publicly available.
Melanoma is a form of cancer that initiates in melanocytes.,Melanoma has multiple phenotypically distinct subpopulation of cells, some of them have embryonic like plasticity which are involved in self-renewal, tumor initiation, metastasis and progression and provide reservoir of therapeutically resistant cells.,Cancer stem cells (CSCs) can be identified and characterized based on various unique cell surface and intracellular markers.,CSCs exhibit different molecular pattern with respect to non-CSCs.,They maintain their stemness and chemoresistant features through specific signaling cascades.,CSCs are weak in immunogenicity and act as immunosupressor in the host system.,Melanoma treatment becomes difficult and survival is greatly reduced when the patient develop metastasis.,Standard conventional oncology treatments such as chemotherapy, radiotherapy and surgical resection are only responsible for shrinking the bulk of the tumor mass and tumor tends to relapse.,Thus, targeting CSCs and their microenvironment niche addresses the alternative of traditional cancer therapy.,Combined use of CSCs targeted and traditional therapies may kill the bulk tumor and CSCs and offer a promising therapeutic strategy for the management of melanoma.