Multidimensional shannon entropy (HM) as an approach to classify H&E colorectal images

被引：0
|
作者：
Segato dos Santos, Luiz Fernando [1 ]
Rozendo, Guilherme Botazzo [1 ]
do Nascimento, Marcelo Zanchetta [2 ]
Azevedo Tosta, Thaina Aparecida [3 ]
da Costa Longo, Leonardo Henrique [1 ]
Neves, Leandro Alves [1 ]
机构：
[1] Sao Paulo State Univ, Dept Comp Sci & Stat DCCE, Sao Jose Do Rio Preto, Brazil
[2] Fed Univ Uberrandia UFU, Fac Comp Sci FACOM, Uberlandia, MG, Brazil
[3] Fed Univ Sao Paulo UNIFESP, Sci & Technol Inst, Sao Jose Dos Campos, Brazil
来源：
2022 29TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP) | 2022年
关键词：
shannon entropy; multiscale; multidimensional; combination; colorectal images; FRACTAL DIMENSION;
D O I：
10.1109/IWSSIP55020.2022.9854438
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this work, we have proposed a method that combines multiscale and multidimensional approaches with Shannon entropy, named H-M. The method was combined with other fractal and sample entropy techniques and tested on H&E colorectal images. The results provided an accuracy of 95.36% for the combination H-M and SampEn(MF). The combinations and analyses presented here are important contributions to the Literature focused on the investigation of techniques for the development of computer-aided diagnosis.
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页数：4
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