An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model

2020 ◽  
pp. 181-193
Author(s):  
Mahantesh Mathapati ◽  
S. Chidambaranathan ◽  
Abdul Wahid Nasir ◽  
G. Vimalarani ◽  
S. Sheeba Rani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Classification Model ◽  
Internet Of Medical Things

scholarly journals A hybrid ensemble deep learning approach for reliable breast cancer detection

2021 ◽  
Vol 7 (2) ◽  
pp. 112
Author(s):  
Mohamed Abdelmoneim Elshafey ◽  
Tarek Elsaid Ghoniemy
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Regression Model ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Fine Tuning ◽  
Classification Model ◽  
Support Vector ◽  
Augmentation Techniques

Among the cancer diseases, breast cancer is considered one of the most prevalent threats requiring early detection for a higher recovery rate. Meanwhile, the manual evaluation of malignant tissue regions in histopathology images is a critical and challenging task. Nowadays, deep learning becomes a leading technology for automatic tumor feature extraction and classification as malignant or benign. This paper presents a proposed hybrid deep learning-based approach, for reliable breast cancer detection, in three consecutive stages: 1) fine-tuning the pre-trained Xception-based classification model, 2) merging the extracted features with the predictions of a two-layer stacked LSTM-based regression model, and finally, 3) applying the support vector machine, in the classification phase, to the merged features. For the three stages of the proposed approach, training and testing phases are performed on the BreakHis dataset with nine adopted different augmentation techniques to ensure generalization of the proposed approach. A comprehensive performance evaluation of the proposed approach, with diverse metrics, shows that employing the LSTM-based regression model improves accuracy and precision metrics of the fine-tuned Xception-based model by 10.65% and 11.6%, respectively. Additionally, as a classifier, implementing the support vector machine further boosts the model by 3.43% and 5.22% for both metrics, respectively. Experimental results exploit the efficiency of the proposed approach with outstanding reliability in comparison with the recent state-of-the-art approaches.

scholarly journals Deep learning model for fully automated breast cancer detection system from thermograms

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262349
Author(s):  
Esraa A. Mohamed ◽  
Essam A. Rashed ◽  
Tarek Gaber ◽  
Omar Karam
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Detection System ◽  
Diagnostic Technique ◽  
Learning Model ◽  
The Body ◽  
Breast Cancer Detection ◽  
Benchmark Database ◽  
Deep Learning Model

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

scholarly journals Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Angel Cruz-Roa ◽  
Hannah Gilmore ◽  
Ajay Basavanhally ◽  
Michael Feldman ◽  
Shridar Ganesan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Invasive Breast Cancer ◽  
Breast Cancer Detection ◽  
Learning Approach ◽  
Tumor Extent ◽  
Whole Slide Images
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