Extended Deep-Learning Network for Histopathological Image-Based Multiclass Breast Cancer Classification Using Residual Features

被引:8
作者
Mewada, Hiren [1 ]
机构
[1] Prince Mohammad Bin Fahd Univ, Dept Elect Engn, POB 1664, Al Khobar 31952, Saudi Arabia
来源
SYMMETRY-BASEL | 2024年 / 16卷 / 05期
关键词
cancer; deep learning; image classification; health risk; decision making; artificial intelligence;
D O I
10.3390/sym16050507
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Autonomy of breast cancer classification is a challenging problem, and early diagnosis is highly important. Histopathology images provide microscopic-level details of tissue samples and play a crucial role in the accurate diagnosis and classification of breast cancer. Moreover, advancements in deep learning play an essential role in early cancer diagnosis. However, existing techniques involve unique models for each classification based on the magnification factor and require training numerous models or using a hierarchical approach combining multiple models irrespective of the focus of the cell features. This may lead to lower performance for multiclass categorization. This paper adopts the DenseNet161 network by adding a learnable residual layer. The learnable residual layer enhances the features, providing low-level information. In addition, residual features are obtained from the convolution features of the preceding layer, which ensures that the future size is consistent with the number of channels in DenseNet's layer. The concatenation of spatial features with residual features helps better learn texture classification without the need for an additional texture feature extraction module. The model was validated for both binary and multiclass categorization of malignant images. The proposed model's classification accuracy ranges from 94.65% to 100% for binary and multiclass classification, and the error rate is 2.78%. Overall, the suggested model has the potential to improve the survival of breast cancer patients by allowing precise diagnosis and therapy.
引用
收藏
页数:17
相关论文
共 39 条
[1]   Going deeper: magnification-invariant approach for breast cancer classification using histopathological images [J].
Alkassar, S. ;
Jebur, Bilal A. ;
Abdullah, Mohammed A. M. ;
Al-Khalidy, Joanna H. ;
Chambers, J. A. .
IET COMPUTER VISION, 2021, 15 (02) :151-164
[2]   A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images [J].
Asare, Sarpong Kwadwo ;
You, Fei ;
Nartey, Obed Tettey .
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2020, 2020
[3]   Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks [J].
Bardou, Dalal ;
Zhang, Kun ;
Ahmad, Sayed Mohammad .
IEEE ACCESS, 2018, 6 :24680-24693
[4]   A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images [J].
Boumaraf, Said ;
Liu, Xiabi ;
Zheng, Zhongshu ;
Ma, Xiaohong ;
Ferkous, Chokri .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 63
[5]   Xception: Deep Learning with Depthwise Separable Convolutions [J].
Chollet, Francois .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :1800-1807
[6]   Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning [J].
Eltoukhy, Mohamed Meselhy ;
Hosny, Khalid M. ;
Kassem, Mohamed A. .
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
[7]   Determining breast cancer biomarker status and associated morphological features using deep learning [J].
Gamble, Paul ;
Jaroensri, Ronnachai ;
Wang, Hongwu ;
Tan, Fraser ;
Moran, Melissa ;
Brown, Trissia ;
Flament-Auvigne, Isabelle ;
Rakha, Emad A. ;
Toss, Michael ;
Dabbs, David J. ;
Regitnig, Peter ;
Olson, Niels ;
Wren, James H. ;
Robinson, Carrie ;
Corrado, Greg S. ;
Peng, Lily H. ;
Liu, Yun ;
Mermel, Craig H. ;
Steiner, David F. ;
Chen, Po-Hsuan Cameron .
COMMUNICATIONS MEDICINE, 2021, 1 (01)
[8]   A framework for distinguishing benign from malignant breast histopathological images using deep residual networks [J].
Gandomkar, Ziba ;
Brennan, Patrick C. ;
Mello-Thoms, Claudia .
14TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI 2018), 2018, 10718
[9]   MuDeRN: Multi-category classification of breast histopathological image using deep residual networks [J].
Gandomkar, Ziba ;
Brennan, Patrick C. ;
Mello-Thoms, Claudia .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2018, 88 :14-24
[10]   Residual learning based CNN for breast cancer histopathological image classification [J].
Gour, Mahesh ;
Jain, Sweta ;
Kumar, T. Sunil .
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2020, 30 (03) :621-635