Convolutional neural network improvement for breast cancer classification
By Fung Fung Ting, Yen Jun Tan, Kok Swee Sim. Expert systems with applications, 2019
376 citations recorded by the source corpus.
Abstract 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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/9c795d2f9e945354fdf7363900df364e7cbc4aa0
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
Citations in the corpus
- A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique (2021, methodology): “[80] implemented a deep CNN for BC-lesion classification.”