Skin Lesion Diagnosis Using Deep Learning Providing reliable computer aid to experts in medical fields is a very important objective as it can improve the quality of healthcare, as well as reduce the associated costs. Most models used in medical diagnosis use out of the box models, pretrained on the ImageNet dataset. In this paper we introduce a deep learning model based on the InceptionResNet network. The model is capable of classifying skin lesions from dermoscopic images with good accuracy (83.96%), surpassing previously obtained results of 81.33% and 78%. We analyze the effectiveness of a few augmentation methods for increasing the performance of the model. We also discuss the results obtained in our experiments and we present several potential ways that can be explored in order to increase the precision, recall and accuracy of the model.

Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.