Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images
Flavia Grignaffini,
Maurizio Troiano,
Francesco Barbuto,
Patrizio Simeoni,
Fabio Mangini,
Gabriele D’Andrea,
Lorenzo Piazzo,
Carmen Cantisani,
Noah Musolff,
Costantino Ricciuti,
Fabrizio Frezza
Affiliations
Flavia Grignaffini
Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza” University of Rome, 00184 Rome, Italy
Maurizio Troiano
Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza” University of Rome, 00184 Rome, Italy
Francesco Barbuto
Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza” University of Rome, 00184 Rome, Italy
Patrizio Simeoni
National Transport Authority (NTA), D02WT20 Dublin, Ireland
Fabio Mangini
Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza” University of Rome, 00184 Rome, Italy
Gabriele D’Andrea
Department of Statistical Sciences, “La Sapienza” University of Rome, 00185 Rome, Italy
Lorenzo Piazzo
Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza” University of Rome, 00184 Rome, Italy
Carmen Cantisani
Dermatology Unit, Department of Clinical Internal Anesthesiologic Cardiovascular Sciences, “La Sapienza” University of Rome, 00185 Rome, Italy
Noah Musolff
Dermatology Unit, Department of Clinical Internal Anesthesiologic Cardiovascular Sciences, “La Sapienza” University of Rome, 00185 Rome, Italy
Costantino Ricciuti
Department of Statistical Sciences, “La Sapienza” University of Rome, 00185 Rome, Italy
Fabrizio Frezza
Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza” University of Rome, 00184 Rome, Italy
Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of M is critical to increase patient survival rates; however, its clinical evaluation is limited by the long timelines, variety of interpretations, and difficulty in distinguishing it from nevi (N) because of striking similarities. To overcome these problems and to support dermatologists, several machine-learning (ML) and deep-learning (DL) approaches have been developed. In the proposed work, melanoma detection, understood as an anomaly detection task with respect to the normal condition consisting of nevi, is performed with the help of a convolutional neural network (CNN) along with the handcrafted texture features of the dermoscopic images as additional input in the training phase. The aim is to evaluate whether the preprocessing and segmentation steps of dermoscopic images can be bypassed while maintaining high classification performance. Network training is performed on the ISIC2018 and ISIC2019 datasets, from which only melanomas and nevi are considered. The proposed network is compared with the most widely used pre-trained networks in the field of dermatology and shows better results in terms of classification and computational cost. It is also tested on the ISIC2016 dataset to provide a comparison with the literature: it achieves high performance in terms of accuracy, sensitivity, and specificity.
