Recognition of Skin Lesions in Dermoscopic Images using Local Binary Patterns and Multinomial Logistic Regression

Skin cancer, such as melanoma, affects more than 13 million people annually and causes more than 65,000 deaths. As with almost all types of cancer, it can be treated effectively if it's diagnosed in it's early stages. The development of computer-aided diagnostic tools is, therefore, a valuable and urgent need to suport the decision of dermatologists, minimize human error, and accelerate the time to diagnosis and the prognosis of patients. The present work proposes the automatic recognition of three skin lesions, namely Common Nevus, Atypical Nevus, and Melanoma, from dermoscopic images. First, a feature vector representing texture is extracted from the dermoscopic images using the local binary pattern operator (LBP). Then, a multinomial logistic regression (MRL) model is used to exploit the feature vector to perform the multiclass classification. The hyperparameters of the LBP operator, namely the radius and the pixel neighborhoods, were modified to provide the feature set to the MRL model that maximizes the classification performance. Moreover, to assure the generalizability of the model and the reproducibility of the results, a 10-iteration Monte Carlo cross-validation (MCCV) was employed, by dividing the dataset into 70% for training and 30% for testing, with different random seeds in each iteration. The optimal LBP-MRL classification model was finally embeddeb in a graphical user interface (GUI) to facilitate the interaction between the user and the classification system. An average accuracy of 90%, averge recall of 89.23%, 84.75% and 100%, and average precision of 89.95%, 88.12% and 100% were obtained, to identify Common Nevus, Atypical Nevus and Melanoma, respectively, in the MCCV strategy. It should be highlighted that the proposed classification system can perfectly differentiate melanoma from the other two skin lesions.