Pathological Image Classification of Breast Cancer Based on Residual Network and Focal Loss

2019 ◽  
Author(s):  
Wenting Shu ◽  
Shaoyu Wang ◽  
Qiang Chen ◽  
Yun Hu ◽  
Zhengwei Cai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Residual Network ◽  
Pathological Image

scholarly journals Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network

2021 ◽  
Vol 11 (1) ◽  
pp. 70-77
Author(s):  
Wahyudi Setiawan
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Training Data ◽  
Unbalanced Data ◽  
Residual Network ◽  
Average Precision ◽  
Average Recall ◽  
Average Accuracy ◽  
Optimal Classification

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of  8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 400x . The classification uses Residual Network (ResNet) 18 and 50. The best results are obtained on images with a magnification of 400x. Classification results using ResNet18 has an average accuracy of 79.82%, an average precision of 71.39%, and an average recall of 82.37%. Meanwhile using ResNet50 showed an average accuracy of 81.67%, average precision of 78.41%, and an average recall of 82.91%.

scholarly journals A Review of Breast Cancer Histopathological Image Classification

2021 ◽  
Vol 2129 (1) ◽  
pp. 012049
Author(s):  
Lei Huang ◽  
Azlan Mohd Zain ◽  
Kai-Qing Zhou ◽  
Chang-Feng Chen
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Histopathological Examination ◽  
Breast Cancer Diagnosis ◽  
Advantages And Disadvantages ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Classification Of Images ◽  
Histopathological Image Classification

Abstract Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

scholarly journals Optimal Design of Deep Residual Network Based on Image Classification of Fashion-MNIST Dataset

2020 ◽  
Vol 1624 ◽  
pp. 052011
Author(s):  
Yusi Tang ◽  
Hanguo Cui ◽  
Shuyong Liu
Keyword(s):  
Optimal Design ◽  
Image Classification ◽  
Residual Network

Classification of histopathological images of breast cancer using an improved convolutional neural network model

2021 ◽  
pp. 1-12
Author(s):  
Yunfeng Yang ◽  
Chen Guan
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Image Classification ◽  
Network Model ◽  
Learning Model ◽  
Image Features ◽  
Data Set ◽  
Study Results ◽  
Deep Learning Model

The accurately automatic classification of medical pathological images has always been an important problem in the field of deep learning. However, the traditional manual extraction of features and image classification usually requires in-depth knowledge and more professional researchers to extract and calculate high-quality image features. This kind of operation generally takes a lot of time and the classification effect is not ideal. In order to solve these problems, this study proposes and tests an improved network model DenseNet-201-MSD to accomplish the task of classification of medical pathological images of breast cancer. First, the image is preprocessed, and the traditional pooling layer is replaced by multiple scaling decomposition to prevent overfitting due to the large dimension of the image data set. Second, the BN algorithm is added before the activation function Softmax and Adam is used in the optimizer to optimize performance of the network model and improve image recognition accuracy of the network model. By verifying the performance of the model using the BreakHis dataset, the new deep learning model yields image classification accuracy of 99.4%, 98.8%, 98.2%and 99.4%when applying to four different magnifications of pathological images, respectively. The study results demonstrate that this new classification method and deep learning model can effectively improve accuracy of pathological image classification, which indicates its potential value in future clinical application.

Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer

2020 ◽  
Vol 508 ◽  
pp. 405-421 ◽  
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
Sonal Saxena ◽  
K. Lakshmanan ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Human Breast Cancer ◽  
Feature Learning ◽  
Mammary Tumors ◽  
Human Breast ◽  
Deep Feature ◽  
Histopathological Image ◽  
Histopathological Image Classification

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Ultrasound image classification of non-mass abnormalities of the breast

Choonpa Igaku ◽  
2018 ◽  
Vol 45 (4) ◽  
pp. 355-360
Author(s):  
Takanori WATANABE
Keyword(s):  
Image Classification ◽  
Ultrasound Image
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