Haixia Liu1, Guozhong Cui2, Yi Luo3, Yajie Guo1, Lianli Zhao4, Yueheng Wang5, Abdulhamit Subasi6,7, Sengul Dogan8, Turker Tuncer8. 1. Department of Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China. 2. Department of Surgical Oncology, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China. 3. Medical Statistics Room, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, People's Republic of China. 4. Department of Internal Medicine teaching and research group, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061000, China. 5. Department of Ultrasound, The Second Hospital of Hebei MedicalUniversity, Shijiazhuang, Hebei Province, 050000, People's Republic of China. 6. Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, 20520, Finland. 7. Department of Computer Science, College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia. 8. Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, 23119, Turkey.
Abstract
Purpose: Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances. Patients and Methods: This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results: The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal. Conclusion: The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.
Purpose: Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances. Patients and Methods: This work presents a new deep feature generation technique for breast cancer detection using BUS images. The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generation phase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results: The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal. Conclusion: The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.
Keywords:
breast ultrasonography (BUS); deep classification framework; deep neural network; grid-based deep feature generator; iterative feature selection
Authors: Paul E Goss; James N Ingle; Silvana Martino; Nicholas J Robert; Hyman B Muss; Martine J Piccart; Monica Castiglione; Dongsheng Tu; Lois E Shepherd; Kathleen I Pritchard; Robert B Livingston; Nancy E Davidson; Larry Norton; Edith A Perez; Jeffrey S Abrams; Patrick Therasse; Michael J Palmer; Joseph L Pater Journal: N Engl J Med Date: 2003-10-09 Impact factor: 91.245