Author: Benedetti, Priscilla; Perri, Damiano; Simonetti, Marco; Gervasi, Osvaldo; Reali, Gianluca; Femminella, Mauro
Title: Skin Cancer Classification Using Inception Network and Transfer Learning Cord-id: pu6la9d7 Document date: 2020_8_24
ID: pu6la9d7
Snippet: 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
Document: 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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