Mammogram images are broadly categorized into two types: carniocaudal (CC) view and mediolateral oblique (MLO) view. In this paper, we study the effect of different image views for mammogram mass classification. For the experiments, we consider a dataset of 328 CC view images and 334 MLO ...
Screening mammography provides two views for each breast: Medio-Lateral Oblique (MLO) and Cranial-Caudal (CC) views. However, current content based image r... A Verikas,P Radeva,D Nikolaev,... - Eighth International Conference on Machine Vision 被引量: 5发表: 2015年 [Lecture Notes in Com...
While two-view mammography taking both mediolateral-oblique (MLO) and cranio-caudual (CC) views is the current standard method of examination in breast cancer screening, single-view mammography is still being performed in some countries on women of specific ages. The rate of cancer detection is ...
According to the outcomes of this suggested method, the detection accuracy of the CC and MLO views for breast cancer was 88.0% and 80.5%, respectively. Alruwaili et al. [31] presented a framework that emphasized transferable learning. In order to avoid overfitting and achieve reliable results,...
Extraneous light and glare should be eliminated for optimal viewing conditions. Mammograms should be arranged in the same manner at each interpretation session to minimize left-right confusion. Routine mammograms should include craniocaudal (CC) and mediolateral oblique (MLO) views. That is, the 2 ...
Classification of masses is based on single view (CC) gives less accuracy than multi-view (CC&MLO).Here we used both views for effective classification. Five features from both the views are selected for the classification. Based on that features we have done classification.P.Sudharsan...
Intuitively, if mass segmentation or detection is robustly performed, prediction results achieved on CC and MLO views should be consistent. Exploiting the inter-view consistency is hence a good way to guide the sampling mechanism which iteratively selects the next image pairs to be labeled by an ...
Focusing on the classification of mammograms using craniocaudal (CC) and mediolateral oblique (MLO) views and their respective mass and micro-calcification segmentations of the same breast, we initially train a separate CNN model for each view and each segmentation map using an Imagenet pre-trained...
The database contains of 136 normal and 130 abnormal i.e., in MLO or CC view. The specific dataset is carefully selected such that the abnormality is apparent in one view and subtle in other due to its complex texture. The proposed system gives 94.76% and 91.31% youdens ratio, 97.41%,...
In this work, we propose a new multi-tasking framework that combines craniocaudal (CC) and mediolateral-oblique (MLO) mammograms for automatic breast mass detection. Rather than addressing mass recognition only, we exploit multi-tasking properties of deep networks to jointly learn mass matching and ...