| Title |
MaskSLIC based CNN Classification Model for Mammogram Feature Extraction |
| Authors |
박진혁(Jin Hyeok Park) ; 이병대(Byeong Dae Lee) ; 선우명훈(Myung Hoon Sunwoo) |
| DOI |
https://doi.org/10.5573/ieie.2021.58.10.59 |
| Keywords |
Deep learning; CNN; SLIC; Superpixel; Medical imaging |
| Abstract |
Mammography is the most effective means for early diagnosis of breast cancer, but the accuracy of diagnosis tends to depend on the skill level of radiologists. CNN-based breast cancer research, which appeared to solve this problem, has a problems that it is dependent on radiologists and difficult to efficiently extract features depending on the input image. In this paper, focusing on the fact that there is a higher possibility of lesions in bright pixels compared to glandular tissue, the superpixel mask image generated by the maskSLIC algorithm reduces the dependence of the doctor on diagnosis and efficiently extracts the features of the entire image. For each preprocessed image, a network was constructed to determine whether the lesion was malignant through the feature extraction layer and classification layer. As a result of the experiment, the GoogLeNet-based model showed the best performance with an accuracy of 0.8026 and AUC 0.8634, and improved performance by 4.48~7.6% and AUC 4.63~8.34% compared to the previous study results. |