A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

2019 ◽  
Vol 125 ◽  
pp. 1-6 ◽  
Author(s):  
SanaUllah Khan ◽  
Naveed Islam ◽  
Zahoor Jan ◽  
Ikram Ud Din ◽  
Joel J. P. C Rodrigues
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning

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.

scholarly journals Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 445 ◽  
Author(s):  
Laith Alzubaidi ◽  
Omran Al-Shamma ◽  
Mohammed A. Fadhel ◽  
Laith Farhan ◽  
Jinglan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Cancer Classification ◽  
Learning Models ◽  
Proposed Model ◽  
Domain Transfer ◽  
Domain Transfer Learning

Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

Sign in / Sign up

Export Citation Format

Share Document