Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low resources requirements. Classification is done by using a pretrained convolutional neural network. We evaluate the accuracy and performance of the proposal and illustrate possible extensions.
Skin Cancer Classification Using Inception Network and Transfer Learning / Priscilla Benedetti; Damiano Perri; Marco Simonetti; Osvaldo Gervasi; Gianluca Reali; Mauro Femminella. - ELETTRONICO. - LNTCS 12249:(2020), pp. 536-545. (Intervento presentato al convegno International Conference on Computational Science and Its Applications tenutosi a Cagliari nel 01/07/2020 - 04/07/2020) [10.1007/978-3-030-58799-4_39].
Skin Cancer Classification Using Inception Network and Transfer Learning
Damiano Perri
;Marco Simonetti
;
2020
Abstract
Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low resources requirements. Classification is done by using a pretrained convolutional neural network. We evaluate the accuracy and performance of the proposal and illustrate possible extensions.
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2111.02402.pdf
accesso aperto
Descrizione: Skin Cancer
Tipologia:
Versione finale referata (Postprint, Accepted manuscript)
Licenza:
Creative commons
Dimensione
1.02 MB
Formato
Adobe PDF
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1.02 MB | Adobe PDF |
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