Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

Abstract

The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROIs containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The authors' results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.

Support Vector Machines for Diagnosis of Breast Tumors on US Images

“…We can say that the set S is linearly separable if there is at least one hyperplane satisfying Equation (4). Meanwhile, we can rescale w and b so that…”

“…Early diagnosis requires an accurate and reliable diagnostic procedure that allows physicians to distinguish benign from malignant breast tumors, and finding such a procedure is an important goal. Current procedures for early detection and diagnosis of breast cancer include self-examination, mammography (2)(3)(4), and ultrasonography (US) (5). Previously, the recommended role of US was limited to differentiating between cysts and solid masses, evaluating masses in a dense breast, and guiding interventional procedures.…”

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

“…There were many studies conducted for automated detection of regions having the characteristics of a specific disease. The diseases taken into consideration in this respect include colorectal dysplasia (Hamilton et al, 1997), breast lesions (Sahiner et al, 1996;Dundar et al, 2010Dundar et al, , 2011, renal cell carcinoma (Waheed et al, 2007), cervical (Hallouche et al, 1992), prostate (Diamond et al, 1982;Pitts et al, 1993;Doyle et al, 2006Doyle et al, , 2007Huang and Lee, 2009), oral cancers (Muthu et al, 2012;Mookiah et al, 2011) and colon cancers (Hamilton et al, 1987;Nasser Esgiar et al, 1998;Rajpoot and Rajpoot, 2004;Masood et al, 2006;Filippas et al, 2003;Nwoye et al, 2006).…”

An Assessment of Imaging Informatics for Precision Medicine in Cancer

“…These methodologies have been around for a long time, and as far back as the early 90s, during the early days of digital imaging, they were used in a variety of applications, such as the detection of lung nodules [105,106], and classification of regions of interest (ROIs) from mammograms as benign or malignant [107]. However, it was not until very recently that deep learning gained popularity and has emerged as one of the most promising tools for image classification.…”