Invasive Ductal Carcinoma (IDC) Classification Based on Breast Histopathology Images Using Convolutional Neural Network

2021 ◽  
pp. 477-486
Author(s):  
Nor Kumalasari Caecar Pratiwi ◽  
Yunendah Nur Fu’adah ◽  
Nur Ibrahim ◽  
Syamsul Rizal ◽  
Sofia Saidah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Histopathology Images

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.

scholarly journals Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss

2019 ◽  
Vol 11 (2) ◽  
pp. 43
Author(s):  
Samuel Aji Sena ◽  
Panca Mudjirahardjo ◽  
Sholeh Hadi Pramono
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Ductal Carcinoma ◽  
Detection System ◽  
Breast Cancer Detection ◽  
University Of Pennsylvania ◽  
Training Approach

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.

scholarly journals Boosting Breast Cancer Detection Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Saad Awadh Alanazi ◽  
M. M. Kamruzzaman ◽  
Md Nazirul Islam Sarker ◽  
Madallah Alruwaili ◽  
Yousef Alhwaiti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Ductal Carcinoma ◽  
Automatic Identification ◽  
Life Threatening ◽  
Breast Cells ◽  
Quantitative Results ◽  
Validation Tests

Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.

Sign in / Sign up

Export Citation Format

Share Document