Classification and Diagnosis of Invasive Ductal Carcinoma Using Deep Learning

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.

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Enhanced Deep Learning Approach for Predicting Invasive Ductal Carcinoma from Histopathology Images

2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) ◽

10.1109/icaibd.2019.8837044 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aiza M. Romano ◽

Alexander A. Hernandez

Keyword(s):

Deep Learning ◽

Invasive Ductal Carcinoma ◽

Ductal Carcinoma ◽

Learning Approach ◽

Histopathology Images

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201702 ◽

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.

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

10.21203/rs.3.rs-478654/v1 ◽

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.

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