12th International Conference of the International Biometric Society's Eastern Mediterranean Region, İzmir, Türkiye, 8 - 11 Mayıs 2023, ss.69
ABNORMALITY DETECTION AND CLASSIFICATION ON MAMMOGRAPHY IMAGES VIA CONVOLUTIONAL NEURAL NETWORKS
Breast cancer is the most common type of cancer among women. Digital mammography screening is
an imaging method that helps determine the rate of early detection of breast cancer. In the last two
decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyze
screening mammograms. Since 2012, deep convolutional neural networks (CNN) have been shown to
achieve high performances in image recognition. In this study, the effectiveness of deep learning using
Convolutional Neural Networks (CNN) was tested on digital mammography images in detecting the
presence of lesions (abnormal/normal). In addition, classification performances of different CNN
architectures will be compared. Digital mammography images were used for this study, which
received ethics committee permission from Hacettepe University. Craniocaudal (CC) view were
carried out for each breast. Spyder environment (with the Keras and TensorFlow) was used for image
processing steps (pre-processing, segmentation, feature selection, classification) and evaluating the
performance on the classification. The data set was divided into two as training and test sets with 80%
and 20%. Noise was removed from the images with image pre-processing techniques. Then, the
regions of interest (ROI) obtained after segmentation were determined by CNN with linear filters and
activation functions. The features were obtained from the ROIs with the help of the GLCM matrix. So
far, the performance of 87 lesioned image (malign and benign images) 59 lesion less images (normal
image) in the CNN model was examined. The applied CNN model reached 79.64%, 80.85% and
75.44% performance rates for accuracy, sensitivity and specificity, respectively. By increasing the
number of observations, the analyzes will be renewed and also the classification performances of
different CNN architectures will be compared.
Keywords: Mammography classification, Deep learning, Medical imaging processing, Computeraided detectio