Entropy Role on Patch-Based Binary Classification for Skin Melanoma

被引：1

作者：

Lachaud, Guillaume ^{[1
]}

Conde-Cespedes, Patricia ^{[1
]}

Trocan, Maria ^{[1
]}

机构：

[1] ISEP Inst Super Elect Paris, 10 Rue Vanves, F-92130 Issy Les Moulineaux, France

来源：

ADVANCES IN COMPUTATIONAL COLLECTIVE INTELLIGENCE (ICCCI 2021) | 2021年 / 1463卷

关键词：

Entropy; Skin melanoma; Patch-based classification; Resnet; CANCER;

D O I：

10.1007/978-3-030-88113-9_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we split the region of interest of dermoscopic images of skin lesions in patches of different size and we analyze the impact of the entropy of the patches on patch-based binary classification using a convolutional neural network (CNN). Specifically, we analyze the distribution of entropy amongst the patches and we compare the training time of a classifier on subsets of the data with varying entropy. We find that the classifier converges faster on patches with higher entropy. Our entropy-based analysis is performed on skin lesion images from the ISIC archive.

引用

页码：324 / 333

页数：10

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