Combining Deep Learning with Traditional Features for Classification and Segmentation of Pathological Images of Breast Cancer

He, Simin; Ruan, Jing; Long, Yi; Wang, Jianlian; Wu, Chenchen; Ye, Guanglu; Zhou, Jingfan; Yue, Junqiu; Zhang, Yanggeling

doi:10.1109/iscid.2018.00007

Cited by 17 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Alex-Net, we fine-tuned the training parameters and trained Alex-Net by ImageNet. Then, we extracted the features from the histopathology images via the “fc7” layer and obtained a 4096-dimensional vector for each image [ 42 ]. We terminated the training after 20 epochs when the validation accuracy did not improve.…”

Section: Resultsmentioning

confidence: 99%

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Cai

Liu

Zhang

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values, with many computer-aided diagnosis systems using common deep learning methods that have been proposed to save time and labour. Even though deep learning methods are an end-to-end method, they perform exceptionally well given a large dataset and often show relatively inferior results for a small dataset. In contrast, traditional feature extraction methods have greater robustness and perform well with a small/medium dataset. Moreover, a texture representation-based global approach is commonly used to classify histological tissue images expect in explicit segmentation to extract the structure properties. Considering the scarcity of medical datasets and the usefulness of texture representation, we would like to integrate both the advantages of deep learning and traditional machine learning, i.e., texture representation. To accomplish this task, we proposed a classification model to detect renal cancer using a histopathology dataset by fusing the features from a deep learning model with the extracted texture feature descriptors. Here, five texture feature descriptors from three texture feature families were applied to complement Alex-Net for the extensive validation of the fusion between the deep features and texture features. The texture features are from (1) statistic feature family: histogram of gradient, gray-level cooccurrence matrix, and local binary pattern; (2) transform-based texture feature family: Gabor filters; and (3) model-based texture feature family: Markov random field. The final experimental results for classification outperformed both Alex-Net and a singular texture descriptor, showing the effectiveness of combining the deep features and texture features in renal cancer detection.

show abstract

Section: Resultsmentioning

confidence: 99%

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Cai

Liu

Zhang

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…He et al [11] presented a classification of mammograms using features extracted using Hough transform. Hough transform is a two-dimensional transform.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Nagalakshmi¹,

Suriya²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.

show abstract

“…TL addresses the problem of cross-domain learning by transmitting relevant knowledge from the source domain to the task domain [ 19 ]. Deep TL is frequently used because of its improved performance and adaptability [ 20 , 21 , 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

et al. 2022

View full text Add to dashboard Cite

Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called ‘HE-HER2Net’ has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

show abstract

Combining Deep Learning with Traditional Features for Classification and Segmentation of Pathological Images of Breast Cancer

Cited by 17 publications

References 6 publications

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

Contact Info

Product

Resources

About