An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set

2020 ◽  
Vol 16 ◽  
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Diagnose Breast Cancer ◽  
Histopathological Images ◽  
And Performance ◽  
Learned Features ◽  
Intelligent Healthcare

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Implementation of a direct patient callback system improves compliance for additional breast imaging and early breast cancer detection

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6073-6073
Author(s):  
D. Richard-Kowalski ◽  
D. Termeulen ◽  
M. Reed ◽  
R. Reyes ◽  
M. Kuliga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Recall Rate ◽  
Screening Mammography ◽  
Breast Cancer Detection ◽  
Detection Methods ◽  
Diagnose Breast Cancer ◽  
Additional Imaging

6073 Background: Existing patient recall systems usually involve contacting the referring physician who then notifies the patient to schedule a return visit for further imaging. We set out to determine whether a direct patient callback system would improve patient compliance in returning for additional imaging including magnification, spot compression, and ultrasound, and whether that would translate to an improvement in early breast cancer detection. Methods: Beginning on 4/1/2004, we prospectively identified all patients whose screening mammograms were read as having an incomplete assessment that required additional imaging (ACR BIRADS 0). Those patients were contacted directly via telephone to return for additional views. Results: Between 11/1/2002 and 3/31/3004, 1142 patients with incomplete screening mammography were identified and the referring physicians were contacted. 956 of 1142 (84%) patients returned and underwent additional breast imaging. Between 4/1/2004 and 12/31/2005, 1,336 patients with incomplete screening mammography were contacted directly to return for additional imaging. 1,307 of 1,336 (98%) patients returned and underwent additional breast imaging. (p < 0.0001, Fisher’s exact test). 125 of the 1,307 (8.5%) of the subsequent exams were found to be suspicious and biopsy was recommended (ACR BIRADS 4 or 5). Conclusions: Our new system of contacting patients with incomplete mammography has significantly increased our recall rate. Implementation of this system has enabled us to identify those patients whose mammograms are suspicious and ultimately diagnose breast cancer earlier. Direct patient callback has become standard policy and we are recommending this system for all radiology recall examinations. No significant financial relationships to disclose.

Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

2020 ◽  
pp. 1-16
Author(s):  
Deepika Kumar ◽  
Usha Batra
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Invasive Ductal Carcinoma ◽  
Cancer Detection ◽  
Ductal Carcinoma ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Learner Model ◽  
Histopathological Images

Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, Machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure.

Image Analysis and Understanding Techniques for Breast Cancer Detection from Digital Mammograms

2014 ◽  
pp. 123-148 ◽  
Author(s):  
Subodh Srivastava ◽  
Neeraj Sharma ◽  
S.K. Singh
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Automated System ◽  
Computer Aided ◽  
Recent Developments ◽  
Cad Tool ◽  
Feature Evaluation And Selection ◽  
And Performance

In this chapter, an overview of recent developments in image analysis and understanding techniques for automated detection of breast cancer from digital mammograms is presented. The various steps in the design of an automated system (i.e. Computer Aided Detection [CADe] and Computer Aided Diagnostics (CADx)] include preparation of image database for classification, image pre-processing, mammogram image enhancement and restoration, segmentation of Region Of Interest (ROI) for cancer detection, feature extraction of selected ROIs, feature evaluation and selection, and classification of selected mammogram images in to benign, malignant, and normal. In this chapter, a detailed overview of the various methods developed in recent years for each stage required in the design of an automated system for breast cancer detection is discussed. Further, the design, implementation and performance analysis of a CAD tool is also presented. The various types of features extracted for classification purpose in the proposed tool include histogram features, texture features, geometric features, wavelet features, and Gabor features. The proposed CAD tool uses fuzzy c-means segmentation algorithm, the feature selection algorithm based on the concepts of genetic algorithm which uses mutual information as a fitness function, and linear support vector machine as a classifier.

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