Breast cancer diagnosis using deep belief networks on ROI images
Gökhan Altan
Abstract
Hand-crafted features are efficient methods for image processing, recognition, and computer vision. However, the advancements in data size and image resolution lead to inconvenience in feature extraction. Moreover, they are unstable, method-dependent, and computationally intensive due to high dimensions. Especially, big data on image datasets causes unpredictable long process. It is a definite necessity to adjust the feature extraction algorithms to computer-assisted methods for image processing. Generative representational learning algorithms have been emerging approaches with the advantages of Deep Learning. In this study, I proposed employing Deep Belief Networks (DBN) for breast cancer diagnosis on ROI images. DBN models were iterated on different image sizes to evaluate the impact of dimensionality on ROI images. The proposed DBN model has achieved performance rates of 96.32%, 96.68%, 95.93%, and 96.40% for accuracy, specificity, sensitivity, and precision, respectively. Consequently, the proposed DBN with detailed representational learning is an efficient and robust algorithm for the classification of breast cancer and healthy tissues on mammograms by the advantage of generative architectures. Elle karlan znitelikler, grnt ileme, tanma ve bilgisayarl gr iin etkili yntemlerdir. Ancak, veri boyutu ve grnt znrlklerindeki art, zniteliklerin elde edilmesinde zorluklara sebep olmutur. Kararsz, ynteme baml ve hesaplama asndan youndurlar. zellikle, grnt veri kmelerindeki byk veriler, ngrlemeyen uzun sreler dourur