The epidemiology of melanoma is complex, and individual risk depends on sun exposure, host factors, and genetic factors, and in their interactions as well. Sun exposure can be classified as intermittent, chronic, or cumulative (overall) exposure, and each appears to have a different effect on type of melanoma. Other environmental factors, such as chemical exposures-either through occupation, atmosphere, or food-may increase risk for melanoma, and this area warrants further study. Host factors that are well known to be important are the numbers and types of nevi and the skin phenotype. Genetic factors are classified as high-penetrant genes, moderate-risk genes, or low-risk genetic polymorphisms. Subtypes of tumors, such as BRAF-mutated tumors, have different risk factors as well as different therapies. Prevention of melanoma has been attempted using various strategies in specific subpopulations, but to date optimal interventions to reduce incidence have not emerged.

Background: Basal cell carcinoma (BCC) is the most common skin cancer, which is highly damaging in its advanced stages. Computer‐aided techniques provide a feasible option for early detection ofBCC. However, automatedBCCdetection techniques immensely rely on handcrafting high‐level precise features. Such features are not only computationally complex to design but can also represent a very limited aspect of the lesion characteristics. This paper proposes an automatedBCCdetection technique that directly learns the features from image data, eliminating the need for handcrafted feature design.Methods: The proposed method is composed of 2 parts. First, an unsupervised feature learning framework is proposed which attempts to learn hidden characteristics of the data including vascular patterns directly from the images. This is done through the design of a sparse autoencoder (SAE). After the unsupervised learning, we treat each of the learned kernel weights of theSAEas a filter. Convolving each filter with the lesion image yields a feature map. Feature maps are condensed to reduce the dimensionality and are further integrated with patient profile information. The overall features are then fed into a softmax classifier forBCCclassification.Results: On a set of 1199BCCimages, the proposed framework achieved an area under the curve of 91.1%, while the visualization of learned features confirmed meaningful clinical interpretation of the features.Conclusion: The proposed framework provides a non‐invasive fastBCCdetection tool that incorporates both dermoscopic lesional features and clinical patient information, without the need for complex handcrafted feature extraction.

Skin cancer is the most common cancer in the Western world. In order to accurately detect the disease, especially malignant melanoma—the most fatal form of skin cancer—at an early stage when the prognosis is excellent, there is an urgent need to develop noninvasive early detection methods. We believe that polarization speckle patterns, defined as a spatial distribution of depolarization ratio of traditional speckle patterns, can be an important tool for skin cancer detection. To demonstrate our technique, we conduct a large in vivo clinical study of 214 skin lesions, and show that statistical moments of the polarization speckle pattern could differentiate different types of skin lesions, including three common types of skin cancers, malignant melanoma, squamous cell carcinoma, basal cell carcinoma, and two benign lesions, melanocytic nevus and seborrheic keratoses. In particular, the fourth order moment achieves better or similar sensitivity and specificity than many well-known and accepted optical techniques used to differentiate melanoma and seborrheic keratosis.

Skin cancer is the most common form of cancer in North America, Australia, and other predominantly fair-skinned populations worldwide, and the incidence of skin cancer has been rapidly increasing over the past several decades.¹ Skin cancer is typically divided into melanoma and nonmelanoma subcategories. The majority of nonmelanoma skin cancers are keratinocyte carcinomas, namely basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), both of which can often lead to disfigurement and further complications if left untreated.² Malignant melanoma (MM) is associated with a significantly higher mortality rate than keratinocyte carcinoma. There is strong correlation between prognosis and melanoma tumor thickness within the skin, highlighting the importance of early detection and treatment for improving patient outcomes and survival.³