Skin Cancer Classification using Inception Network and Transfer Learning

Damiano Perri; Gianluca Reali; Marco Simonetti; Mauro Femminella; Osvaldo Gervasi; Priscilla Benedetti

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Skin Cancer Classification using Inception Network and Transfer Learning

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arxiv 2111.02402 v1 pith:IHITOOHH submitted 2021-11-03 eess.IV cs.CVcs.LG

Priscilla Benedetti , Damiano Perri , Marco Simonetti , Osvaldo Gervasi , Gianluca Reali , Mauro Femminella This is my paper · ORCID

classification eess.IVcs.CVcs.LG

keywords classificationimagesnetworkskinaccuracyapproachcancerchallenging

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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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