Name
Abstract | ||
|---|---|---|
The recognition of breast abnormality, which mainly consists of mass and micro-calcification, plays a critical role in the detection of breast cancer. To facilitate the procedure of making decisions on suspicious regions in mammograms, we proposed a light-weighted deep convolutional neural network (CNN) recognition system termed DisepNet, a light-weighted fully convolutional network that shows promising performance on the detection task of breast abnormality. In the proposed DisepNet, novel blocks feature extraction are designed, which are termed as Disep block and Incep-L block respectively. We evaluated the proposed model by 5-fold cross-validation on a combined dataset, which comes from two public databases MINI-MIAS and INbreast. The final accuracy of our proposed model achieved a mean accuracy at 95.60% while the sensitivity and specificity reached 93.71% and 97.44% respectively. A comparison between our model and existing methods shows that our model provides the best performance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compeleceng.2020.106961 | COMPUTERS & ELECTRICAL ENGINEERING |
Keywords | DocType | Volume |
Breast abnormality, Light-weighted, DisepNet, Disep block, Incep-L | Journal | 90 |
ISSN | Citations | PageRank |
0045-7906 | 0 | 0.34 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Yu | 1 | 35 | 3.58 |
| Kaijian Xia | 2 | 1 | 0.68 |
| yudong zhang | 3 | 1334 | 90.44 |