Convolutional neural network improvement for breast cancer classification
| Autor: | Yen Jun Tan, F. F. Ting, Kok Swee Sim |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
medicine.medical_specialty Receiver operating characteristic Artificial neural network business.industry Supervised learning General Engineering 02 engineering and technology medicine.disease Convolutional neural network Suspected breast cancer Computer Science Applications 020901 industrial engineering & automation Breast cancer Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Medical imaging medicine 020201 artificial intelligence & image processing Radiology skin and connective tissue diseases Breast cancer classification business |
| Zdroj: | Expert Systems with Applications. 120:103-115 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.11.008 |
| Popis: | Traditionally, physicians need to manually delineate the suspected breast cancer area. Numerous studies have mentioned that manual segmentation takes time, and depends on the machine and the operator. The algorithm called Convolutional Neural Network Improvement for Breast Cancer Classification (CNNI-BCC) is presented to assist medical experts in breast cancer diagnosis in timely manner. The CNNI-BCC uses a convolutional neural network that improves the breast cancer lesion classification in order to help experts for breast cancer diagnosis. CNNI-BCC can classify incoming breast cancer medical images into malignant, benign, and healthy patients. The application of present algorithm can assist in classification of mammographic medical images into benign patient, malignant patient and healthy patient without prior information of the presence of a cancerous lesion. The presented method aims to help medical experts for the classification of breast cancer lesion through the implementation of convolutional neural network for the classification of breast cancer. CNNI-BCC can categorize incoming medical images as malignant, benign or normal patient with sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) and specificity of 89.47%, 90.50%, 0.901 ± 0.0314 and 90.71% respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect