Classification of Histopathological Images of Penile Cancer using DenseNet and Transfer Learning

被引：1

作者：

Mendes Lauande, Marcos Gabriel ^{[1
]}

Teles, Amanda Mara ^{[2
]}

da Silva, Leandro Lima ^{[2
]}

Falcao Matos, Caio Eduardo ^{[1
]}

Braz Junior, Geraldo ^{[1
]}

de Paiva, Anselmo Cardoso ^{[1
]}

Sousa de Almeida, Joao Dallyson ^{[1
]}

Gil da Costa Oliveira, Rui Miguel ^{[2
,3
]}

Brito, Haissa Oliveira ^{[2
]}

Nascimento, Ana Giselia ^{[4
]}

Feitosa Pestana, Ana Clea ^{[2
,4
]}

Silva Azevedo dos Santos, Ana Paula ^{[2
]}

Lopes, Fernanda Ferreira ^{[2
]}

机构：

[1] Fed Univ Maranhao UFMA, Comp Appl Grp NCA, Sao Luis, Maranhao, Brazil

[2] Fed Univ Maranhao UFMA, Grad Program Adult Hlth PPGSAD, Sao Luis, Maranhao, Brazil

[3] Univ Tras Os Montes & Alto Douro UTAD, Ctr Res & Technol Agroenvironm & Biol Sci CITAB, Inov4Agro, Vila Real, Portugal

[4] Fed Univ Maranhao UFMA, Dept Pathol, Presidente Dutra Univ Hosp, Sao Luis, Maranhao, Brazil

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 4 | 2022年

关键词：

Histopathology; Penile Cancer; Deep Learning; Deep Features; Convolutional Neural Network; Transfer Learning; Data Augmentation; Contrast Limited Adaptative Histogram Equalization; CONVOLUTIONAL NEURAL-NETWORKS;

D O I：

10.5220/0010893500003124

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Penile cancer is a rare tumor that accounts for 2% of cancer cases in men in Brazil. Histopathological analyzes are commonly used in its diagnosis, making it possible to assess the degree of the disease, its evolution, and its nature. About a decade ago, scientific works in the field of deep learning were developed to help pathologists make decisions quickly and reliably, opening up possibilities for new contributions to improve such a complex and time-consuming activity for these professionals. In this work, we present the development of a method that uses a DenseNet to diagnose penile cancer in histopathological images, and the construction of a dataset (via the Legal Amazon Penis Cancer Project) used to validate this method. In the experiments performed, an Fl-Score of up to 97.39% and a sensitivity of up to 98.33% were achieved in this binary classification problem (normal or squamous cell carcinoma).

引用

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页码：976 / 983

页数：8

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