Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography. (May 2021) - Chouhan, Naveed; Khan, Asifullah; Shah, Jehan Zeb; Hussnain, Mazhar; Khan, Muhammad Waleed
Abstract: Breast cancer is one of the deadly diseases among women. However, the chances of death are highly reduced if it gets diagnosed and treated at its early stage. Mammography is one of the reliable methods used by the radiologist to detect breast cancer at its initial stage. Therefore, an automatic and secure breast cancer detection system that accurately detects abnormalities not only increases the radiologist's diagnostic confidence but also provides more objective evidence. In this work, an automatic Diverse Features based Breast Cancer Detection (DFeBCD) system is proposed to classify a mammogram as normal or abnormal. Four sets of distinct feature types are used. Among them, features based on taxonomic indexes, statistical measures and local binary patterns are static. The proposed DFeBCD dynamically extracts the fourth set of features from mammogram images using a highway-network based deep convolution neural network (CNN). Two classifiers, Support Vector Machine (SVM) and Emotional Learning inspired Ensemble Classifier (ELiEC), are trained on these distinct features using a standard IRMA mammogram dataset. The reliability of the system performance is ensured by applying 5-folds cross-validation. Through experiments, we have observed that the performance of the DFeBCD system on dynamically generated features through highway network-based CNN is better than that of all the three individual sets of ad-hoc features. Furthermore, the hybridization of all four types of features improves the system's performance by nearly 2–3%. The performance of both the classifiers is comparable using the individual sets of ad-hoc features. However, the ELiEC classifier's performance is better than SVM using both hybrid and dynamic features. Highlights: An automatic Diverse Features based Breast Cancer Detection (DFeBCD) system to classify a mammogram as normal or abnormal. The performance of the DFeBCD system is better on the features, generated dynamically through highway network-based CNN. The hybridization of the four types of features improves the performance of the system by nearly 2–3%.