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

被引：2

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

页码：976 / 983

页数：8

共 50 条

[31] Breast cancer images Classification using a new transfer learning technique
Mukhlif A.A.
Al-Khateeb B.
Mohammed M.A.
Iraqi Journal for Computer Science and Mathematics, 2023, 4 (01): : 167 - 180
[32] Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet
Ye, Guanglu
Ruan, Jun
Wu, Chenchen
Zhou, Jingfan
He, Simin
Wang, Jianlian
Zhu, Zhikui
Yue, Junqiu
Zhang, Yanggeling
2019 ELEVENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI 2019), 2019, : 173 - 178
[33] A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images
Boumaraf, Said
Liu, Xiabi
Zheng, Zhongshu
Ma, Xiaohong
Ferkous, Chokri
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 63
[34] Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images
Shankar, K.
Dutta, Ashit Kumar
Kumar, Sachin
Joshi, Gyanendra Prasad
Doo, Ill Chul
CANCERS, 2022, 14 (11)
[35] Classification of Histopathological Images from Breast Cancer Patients Using Deep Learning: A Comparative Analysis
Thalakottor L.A.
Shirwaikar R.D.
Pothamsetti P.T.
Mathews L.M.
Critical Reviews in Biomedical Engineering, 2023, 51 (04) : 41 - 62
[36] Automated Lung and Colon Cancer Classification Using Histopathological Images
Ji, Jie
Li, Jirui
Zhang, Weifeng
Geng, Yiqun
Dong, Yuejiao
Huang, Jiexiong
Hong, Liangli
BIOMEDICAL ENGINEERING AND COMPUTATIONAL BIOLOGY, 2024, 15
[37] Classification of Breast Cancer Histopathological Images Using KAZE Features
Sanchez-Morillo, Daniel
Gonzalez, Jesus
Garcia-Rojo, Marcial
Ortega, Julio
BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2018), PT II, 2019, 10814 : 276 - 286
[38] Automated Detection and Classification of Breast Cancer Tumour Cells using Machine Learning and Deep Learning on Histopathological Images
Yadav, Anju
Verma, Vivek K.
Pal, Vipin
Jain, Vanshika
Garg, Vanshika
2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2021,
[39] Using Transfer Learning with Convolutional Neural Networks to Diagnose Breast Cancer from Histopathological Images
Zhi, Weiming
Yueng, Henry Wing Fung
Chen, Zhenghao
Zandavi, Seid Miad
Lu, Zhicheng
Chung, Yuk Ying
NEURAL INFORMATION PROCESSING (ICONIP 2017), PT IV, 2017, 10637 : 669 - 676
[40] On Convolutional Neural Networks and Transfer Learning for Classifying Breast Cancer on Histopathological Images Using GPU
Silva, D. C. S. E.
Cortes, O. A. C.
XXVII BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2020, 2022, : 1993 - 1998

← 1 2 3 4 5 →