Skin Cancer Classification Using Inception Network and Transfer Learning

被引：12

作者：

Benedetti, Priscilla ^{[1
]}

Perri, Damiano ^{[2
]}

Simonetti, Marco ^{[2
]}

Gervasi, Osvaldo ^{[3
]}

Reali, Gianluca ^{[1
]}

Femminella, Mauro ^{[1
]}

机构：

[1] Univ Perugia, Dept Engn, Perugia, Italy

[2] Univ Florence, Dept Math & Comp Sci, Florence, Italy

[3] Univ Perugia, Dept Math & Comp Sci, Perugia, Italy

来源：

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I | 2020年 / 12249卷

关键词：

Machine learning; Convolutional neural network; Keras; TensorFlow;

D O I：

10.1007/978-3-030-58799-4_39

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：536 / 545

页数：10

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