A multiple circular path convolution neural network system for detection of mammographic masses

Abstract

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.

Computerized Detection of Lesions in Diagnostic Images

“…The class of neural filters was used for image-processing tasks such as edge-preserving noise reduction in fluoroscopy, radiographs and other digital pictures [126,129], edge enhancement from noisy images [128], and enhancement of subjective edges traced by a physician in cardiac images [130]. The class of convolution NNs was applied to classification tasks such as false-positive (FP) reduction in CAD schemes for the detection of lung nodules in chest radiographs (CXRs) [68,69,73], FP reduction in CAD schemes for the detection of microcalcifications [71] and masses [100] in mammography, face recognition [62], and character recognition [88]. The class of MTANNs was used for classification, such as FP reduction in CAD schemes for the detection of lung nodules in CXR [134] and thoracic CT [3,65,121], distinction between benign and malignant lung nodules in CT [131], and FP reduction in a CAD scheme for polyp detection in CT colonography [132,[139][140][141]159].…”

“…Recently, as available computational power has increased dramatically, patch-/ pixel-based machine learning (PML) [114] PMLs were first developed for tasks in medical image processing/analysis and computer vision. There are three classes of PMLs: (1) neural filters [126,129] including neural edge enhancers [128,130], (2) convolution neural networks (NNs) [62,68,69,71,73,88,100] including shift-invariant NNs [153,171,172], and (3) massive-training artificial neural networks (MTANNs) [89,111,120,121,140] including multiple MTANNs [3,121,126,129,131,134], a mixture of expert MTANNs [132,139], a multiresolution MTANN [120], a Laplacian eigenfunction MTANN (LAP-MTANN) [141], and a massive-training support vector regression (MTSVR) [159]. The class of neural filters was used for image-processing tasks such as edge-preserving noise reduction in fluoroscopy, radiographs and other digital pictures [126,129], edge enhancement from noisy images [128], and enhancement of subjective edges traced by a physician in cardiac images [130].…”

Pixel-Based Machine Learning in Medical Imaging

“…The class of neural filters has been used for image-processing tasks such as edge-preserving noise reduction in radiographs and other digital pictures [38, 39], edge enhancement from noisy images [40], and enhancement of subjective edges traced by a physician in left ventriculograms [41]. The class of convolution NNs has been applied to classification tasks such as false-positive (FP) reduction in CAD schemes for detection of lung nodules in chest radiographs (also known as chest X-rays; CXRs) [42–44], FP reduction in CAD schemes for detection of microcalcifications [45] and masses [46] in mammography, face recognition [47], and character recognition [48]. The class of MTANNs has been used for classification, such as FP reduction in CAD schemes for detection of lung nodules in CXR [57] and CT [17, 52, 63], distinction between benign and malignant lung nodules in CT [58], and FP reduction in a CAD scheme for polyp detection in CT colonography [53, 59–62].…”

“…A convolution NN which has subsampling layers has been developed for face recognition [47]. Some convolution NNs have one output unit [48, 79], some have two output units [80], and some have more than two output units [42, 43, 45, 47] for two-class classification.…”

“…The trained convolution NN reduced 79% of FP detections (which is equivalent to 2-3 FPs per patient), while 80% of true-positive detections were preserved. Convolution NNs have been applied to FP reduction in CAD schemes for detection of microcalcifications [45] and masses [46] in mammography. A convolution NN was trained with 34 mammograms for distinguishing microcalcifications from FPs.…”