ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-05042020-113646


Tipo di tesi
Tesi di laurea magistrale
Autore
VALENZANO, RAFFAELE
URN
etd-05042020-113646
Titolo
Non-melanoma skin cancer image classification with convolutional neural networks
Dipartimento
INFORMATICA
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Relatori
relatore Prof. Rinzivillo, Salvatore
Parole chiave
  • non-melanoma skin cancer
  • convolutional neural networks
  • image classification
  • model explanation
  • cascading training
  • model blending
  • grad-cam
  • saliency maps
Data inizio appello
26/06/2020
Consultabilità
Non consultabile
Data di rilascio
26/06/2090
Riassunto
Non-melanoma skin cancers are frequently occurring cancers distinct from melanoma. Nowadays the workload of dermatologists is becoming impossible to manage, an automated approach to pre-screen patients can be a valid help for them.
We used Convolutional Neural Networks to classify non-melanoma skin cancer, focusing on four kinds of skin cancer: Actinic Keratosis, Basal Cell Carcinoma, Squamous Cell Carcinoma and Seborrheic Keratosis. Firstly, we constructed three data sets, each one of them contains a different type of image among contextual, macro and dermoscopic. For each type of image, we developed a network with different styles of training that we called homogenous training and heterogeneous training. We compared our best model with a State of Art model obtaining in some case better performances. Next, we combined three networks, one for each type of image, to improve predictions.
We also addressed the model explanation, a topic that is getting more and more attention, with the two most used methods: Saliency maps and Grad-CAM.
In the end we introduced a real case scenario, adapting the models we developed to a demo application for iOS and Android.
To complete our study, we also compared a multi-class model with a cascading training of binary models.
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