Breast Cancer Lesion Detection and Classification in mammograms using Deep Neural

Abstract

Abstract A method to automatically detect and classify a lesion into either malignant or non-malignant is presented in this work. The dataset used is obtained from INbreast database and are in format of full-field digital mammography (FFDM). Some of the key challenges in detecting cancerous lesion in mammography are the low contrast between cancerous lesion and its surrounding tissues, apparent contrast similarities between lesion and pectoral muscle, presence of calcifications that may disrupt the detection process, and some level of morphological similarities between the lesion and some normal tissues. The work here consists of two main parts. The first part is the image processing section that aims to sample the lesion with intended lesion-to-surrounding ratio (0.4-0.6) and to avoid sampling from unintended regions such as pectoral muscle. Another key challenge is that the database is relatively small while machine learning requires a relatively large dataset. To improve size of samples, eighty fixed-size images (250 pixels x 250 pixels) are randomly cropped out of each of the previously processed image. The second part is to build the machine learning application based on deep neural network framework to classify samples into two classes, malignant and non-malignant. In present work we apply two different frameworks known as Plain and Residual Net (ResNet). Our calculations show that both models can detect a single lesion with more than 90% accuracy and area under ROC curve >0.94.