This study deals with the problem of the recognition of dermatological and exanthematic diseases through the use of deep learning techniques in order to diagnose malignant diseases at an early stage and in general to bring the pathology identified by the models to the attention of the person. A fundamental part of the research was the study of the methodologies present in the state of the art and for this reason in this paper we report the studies considered as most relevant. In this paper, two different types of models are reported, the Convolutional Network Disease (CND) model and the CND-InceptionV3 model using the transfer learning technique. The use of these two models made it possible to carry out an experimental phase in which the performance that can be achieved using the ISIC-archive dataset was analysed. Subsequently, the description of the work carried out for the improvement of the dataset through the association of syntactic-semantic information is reported. Finally, in the last section, conclusions are drawn on the values obtained and future developments that can be made to improve the performance of the models reported are reported.

Crinieri, A., Terzi, L., Ruggeri, F., Matamoros Aragon, R., Epifania, F., Marconi, L. (2021). Recognition of Skin Diseases and Exanthema with Deep Learning Techniques. Intervento presentato a: 1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) - 30 November 2021, Virtual, Milan.

Recognition of Skin Diseases and Exanthema with Deep Learning Techniques

Matamoros Aragon R.;Epifania F.;Marconi L.
2021

Abstract

This study deals with the problem of the recognition of dermatological and exanthematic diseases through the use of deep learning techniques in order to diagnose malignant diseases at an early stage and in general to bring the pathology identified by the models to the attention of the person. A fundamental part of the research was the study of the methodologies present in the state of the art and for this reason in this paper we report the studies considered as most relevant. In this paper, two different types of models are reported, the Convolutional Network Disease (CND) model and the CND-InceptionV3 model using the transfer learning technique. The use of these two models made it possible to carry out an experimental phase in which the performance that can be achieved using the ISIC-archive dataset was analysed. Subsequently, the description of the work carried out for the improvement of the dataset through the association of syntactic-semantic information is reported. Finally, in the last section, conclusions are drawn on the values obtained and future developments that can be made to improve the performance of the models reported are reported.
No
paper
Scientifica
Convolutional Neural Networks; Skin Diseases; Transfer Learning;
English
1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) - 30 November 2021
Crinieri, A., Terzi, L., Ruggeri, F., Matamoros Aragon, R., Epifania, F., Marconi, L. (2021). Recognition of Skin Diseases and Exanthema with Deep Learning Techniques. Intervento presentato a: 1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) - 30 November 2021, Virtual, Milan.
Crinieri, A; Terzi, L; Ruggeri, F; Matamoros Aragon, R; Epifania, F; Marconi, L
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/390686
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
Social impact