To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
DeepBreastCAD: Deep Learning based Computer Aided Diagnosis System for Breast Mammograms |
|
Author |
M. Arfan Jaffar |
| Citation |
Vol. 26 No. 6 pp. 114-122 |
|
Abstract |
Breast cancer is one of the most dangerous, leading and widespread cancers in the world specially in women. For breast analysis, digital mammography is the most suitable tool used to take mammograms for detection of cancer. It has been proved in the literature, that if it can be detected at early and initial stages, then there are many chances to cure timely and efficiently. Therefore, initial screening of mammograms is the most important to detect cancer at initial stages. In this paper, a solution has been proposed by using deep Convolutional Neural Network (CNN) with Support Vector Machine (SVM). Proposed method first perform preprocessing to resize the image so that it can be suitable for CNN and perform enhancement quality of the images can be enhanced. Deep Convolutional Neural Network (CNN) has been used for features extraction and classification with Support Vector Machine (SVM). Standard dataset MIAS and DDMS has been used for validation of the proposed method by generating new images from these datasets by the process of augmentation. Accuracy, Sensitivity, Specificity and area under the receiver operating curve (AUC) has been used as a quantitative measure and compared with state of the art existing methods. Results shows that proposed method has achieved accuracy 93.35% and 93% sensitivity. |
|
Keywords |
segmentation, breast mammograms, classification, texture |
|
URL |
