Detection Of Abnormalities In Mammograms Research Articles

Detection of abnormalities in mammograms using deep features

The mortality rate of breast cancer can be reduced by early diagnosis and treatment. The computer-aided diagnosis (CAD) systems can effectively help physicians identify the early stage of breast cancer. The primary tool for such systems is mammograms. Usually, these images lack high quality. Furthermore, due to the irregular shape of masses, their size variability, and the apparent similarity of the masses and other dense regions of the breast tissue, it is difficult to detect and diagnose the masses. Although many image processing techniques have been presented for diagnosis of breast masses, they have not been quite successful, and this problem has been retained as a challenge yet. In this paper, a method for classifying breast tissues into normal and abnormal (i.e., cancerous) is proposed, which is based on a deep learning approach. It mainly contains a new convolutional neural network (CNN) and a decision mechanism. After a preprocessing phase, a block around each pixel is fed into a trained CNN to determine whether the pixel belongs to normal or abnormal tissues. This results in a binary map for the input suspicious tissue. Afterward, as a decision mechanism, a thresholding technique is applied to a central block on the produced binary map to label it. The new architecture of the CNN, the training scheme of the network, employing the CNN for classifying the pixels of the suspicious regions rather than the entire input, exerting an effective decision mechanism on the output of the CNN and the learning of the model parameters, helped us achieve superior results compared to state-of-the-art methods by reaching 95 and 94.68 percent for AUC and accuracy, respectively.

Automatic detection of abnormalities in mammograms.

BackgroundIn recent years, an increased interest has been seen in the area of medical image processing and, as a consequence, Computer Aided Diagnostic (CAD) systems. The basic purpose of CAD systems is to assist doctors in the process of diagnosis. CAD systems, however, are quite expensive, especially, in most of the developing countries. Our focus is on developing a low-cost CAD system. Today, most of the CAD systems regarding mammogram classification target automatic detection of calcification and abnormal mass. Calcification normally indicates an early symptom of breast cancer if it appears as a small size bright spot in a mammogram image.MethodsBased on the observation that calcification appears as small bright spots on a mammogram image, we propose a new scale-specific blob detection technique in which the scale is selected through supervised learning. By computing energy for each pixel at two different scales, a new feature “Ratio Energy” is introduced for efficient blob detection. Due to the imposed simplicity of the feature and post processing, the running time of our algorithm is linear with respect to image size.ResultsTwo major types of calcification, microcalcification and macrocalcification have been identified and highlighted by drawing a circular boundary outside the area that contains calcification. Results are quite visible and satisfactory, and the radiologists can easily view results through the final detected boundary.ConclusionsCAD systems are designed to help radiologists in verifying their diagnostics. A new way of identifying calcification is proposed based on the property that microcalcification is small in size and appears in clusters. Results are quite visible and encouraging, and can assist radiologists in early detection of breast cancer.

A computer aided detection framework for mammographic images using fisher linear discriminant and nearest neighbor classifier

Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.