Research Library

open-access-imgOpen AccessBreast Cancer Lesion Detection and Classification in mammograms using Deep Neural
Author(s)
Alexander R. J. Silalahi
Publication year2021
Publication title
iop conference series
Resource typeJournals
PublisherIOP Publishing
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.
Subject(s)anatomy , artificial intelligence , artificial neural network , breast cancer , cancer , chemistry , chromatography , computer science , computer vision , deep learning , digital mammography , filter (signal processing) , lesion , mammography , medicine , pathology , pattern recognition (psychology) , pectoral muscle , pixel , radiology , sample (material) , sampling (signal processing)
Language(s)English
eISSN1757-899X
pISSN1757-8981
DOI10.1088/1757-899x/1115/1/012018

Seeing content that should not be on Zendy? Contact us.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here