Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models

2021 ◽  
pp. 104931
Author(s):  
Nusrat Ameen Barsha ◽  
Aimon Rahman ◽  
M.R. C. Mahdy
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Automated Detection ◽  
Learning Models ◽  
Carcinoma Breast

StackBC: Deep learning and transfer learning techniques based stacking approach for accurate Invasive Ductal Carcinoma classification using histology images

2022 ◽  
pp. 1-12
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Samad Wali ◽  
Sultan Ahmad ◽  
Zafar Ali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Early Recognition ◽  
Predictive Performance ◽  
Operating Characteristics ◽  
Data Set ◽  
Learning Techniques

Artificial intelligence (AI) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.

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.

scholarly journals Predicting Invasive Ductal Carcinoma Tissues in Whole Slide Images of Breast Cancer by Using Convolutional Neural Network Model and Multiple Classifiers in Google Colab

2021 ◽  
Author(s):  
Deepa B G ◽  
S. Senthil
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Abnormal Growth ◽  
Multiple Classifiers

Abstract Breast Cancer (BC) is the common type of cancer found in women which is caused due to the abnormal growth of cells in the breast. An early BC detection helps to increase the survival rate of the patient and 80% BC type was Invasive Ductal Carcinoma (IDC) .In this work, a deep learning-based IDC prediction model is proposed with multiple classifiers and CNN (Convolutional Neural Network). The developed deep learning method used a sequential Keras model like conv2D, Maxpooling2D, Dropout, Flatten and Dense. The multiple classifiers are LR (Logistic Regression), RF (Random Forest), K-NN (K-Nearest Neighbors), SVM (Support Vector Machine), Linear SVC, GNB (Gaussian NB) and DT (Decision Tree). The CNN model generated by using SkLearn, Keras and Tensor flow libraries, and results are organized by MatPlot libraries. At the classification stage, a helper function was defined, and Google Colab online browser platform used for developing the proposed model. The performance is analysed in terms of Accuracy, Precision, Recall, F1-score and Support.

Expression of Let-7c in invasive ductal carcinoma of breast cancer and its clinical significance

2009 ◽  
Vol 29 (4) ◽  
pp. 400-403
Author(s):  
Shu-rong SHEN ◽  
Jun-yi SHI ◽  
Xian SHEN ◽  
Guan-li HUANG ◽  
Xiang-yang XUE
Keyword(s):  
Breast Cancer ◽  
Clinical Significance ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma

Uncommon Breast Malignancies: Presentation Pattern, Prognostic Issue and Treatment Outcome in an Italian Single Institution Experience

2013 ◽  
Vol 99 (1) ◽  
pp. 39-44
Author(s):  
Claudia Maria Regina Bareggi ◽  
Dario Consonni ◽  
Barbara Galassi ◽  
Donatella Gambini ◽  
Elisa Locatelli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Outcome ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Early Stage ◽  
Medullary Carcinoma ◽  
Mucinous Carcinoma ◽  
Lymph Node Involvement ◽  
Tubular Carcinoma ◽  
Breast Malignancies

Aims and background Often neglected by large clinical trials, patients with uncommon breast malignancies have been rarely analyzed in large series. Patients and methods Of 2,052 patients diagnosed with breast cancer and followed in our Institution from January 1985 to December 2009, we retrospectively collected data on those with uncommon histotypes, with the aim of investigating their presentation characteristics and treatment outcome. Results Rare histotypes were identified in 146 patients (7.1% of our total breast cancer population), being classified as follows: tubular carcinoma in 75 (51.4%), mucinous carcinoma in 36 (24.7%), medullary carcinoma in 25 (17.1%) and papillary carcinoma in 10 patients (6.8%). Whereas age at diagnosis was not significantly different among the diverse diagnostic groups, patients with medullary and papillary subtypes had a higher rate of lymph node involvement, similar to that of invasive ductal carcinoma. Early stage diagnosis was frequent, except for medullary carcinoma. Overall, in comparison with our invasive ductal carcinoma patients, those with rare histotypes showed a significantly lower risk of recurrence, with a hazard ratio of 0.28 (95% CI, 0.12–0.62; P = 0.002). Conclusions According to our analysis, patients with uncommon breast malignancies are often diagnosed at an early stage, resulting in a good prognosis with standard treatment.

scholarly journals Impact of Pathological Characteristics on Local Relapse After Breast-Conserving Therapy: A Subgroup Analysis of the EORTC Boost Versus No Boost Trial

2009 ◽  
Vol 27 (30) ◽  
pp. 4939-4947 ◽  
Author(s):  
Heather A. Jones ◽  
Ninja Antonini ◽  
Augustinus A.M. Hart ◽  
Johannes L. Peterse ◽  
Jean-Claude Horiot ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Breast Conserving Therapy ◽  
Local Relapse ◽  
High Grade ◽  
Invasive Tumor ◽  
Boost Dose ◽  
Central Pathology ◽  
Pathology Review

Purpose To investigate the long-term impact of pathologic characteristics and an extra boost dose of 16 Gy on local relapse, for stage I and II invasive breast cancer patients treated with breast conserving therapy (BCT). Patients and Methods In the European Organisation for Research and Treatment of Cancer boost versus no boost trial, after whole breast irradiation, patients with microscopically complete excision of invasive tumor, were randomly assigned to receive or not an extra boost dose of 16 Gy. For a subset of 1,616 patients central pathology review was performed. Results The 10-year cumulative risk of local breast cancer relapse as a first event was not significantly influenced if the margin was scored negative, close or positive for invasive tumor or ductal carcinoma in situ according to central pathology review (log-rank P = .45 and P = .57, respectively). In multivariate analysis, high-grade invasive ductal carcinoma was associated with an increased risk of local relapse (P = .026; hazard ratio [HR], 1.67), as was age younger than 50 years (P < .0001; HR, 2.38). The boost dose of 16 Gy significantly reduced the local relapse rate (P = .0006; HR, 0.47). For patients younger than 50 years old and in patients with high grade invasive ductal carcinoma, the boost dose reduced the local relapse from 19.4% to 11.4% (P = .0046; HR, 0.51) and from 18.9% to 8.6% (P = .01; HR, 0.42), respectively. Conclusion Young age and high-grade invasive ductal cancer were the most important risk factors for local relapse, while margin status had no significant influence. A boost dose of 16 Gy significantly reduced the negative effects of both young age and high-grade invasive cancer.

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