Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers

Abstract

AbstractMammography is most popular imaging method used often in predicting breast cancer within women above age of 38 years. Various computer‐assisted algorithms have been employed for classifying breast masses as normal or malignant using screen film mammographic (SFM) images. In present research work, exhaustive experimentations have been carried out with nine deep learning‐based convolutional neural networks (CNNs) belonging to the three different categories of CNN architectures including (a) simple convolution‐based series models, that is VGG16 and VGG19 (b) simple convolution‐based directed acyclic graph (DAG) model, that is GoogleNet and (c) dilated convolution‐based DAG models, that is ResNet18, ResNet50, Inceptionv3, XceptionNet, ShuffleNet and MobileNet‐V2, for binary classification of the mammographic masses with SFM images. The experimental work has been carried out using 518 mammographic images taken from DDSM dataset with 208 images ϵ benign class and 310 images ϵ malignant class, respectively. The encoder‐decoder based semantic segmentation network model, that is ResNet50 has been used for the segmentation of mammographic masses from SFM images. The segmented masses images obtained from the ResNet50 model are subjected to classification experiments. To design a robust hybrid classifier design, that is, deep learning (DL)‐based feature extraction and machine learning (ML)‐based classification, the first step is to obtain an optimal DL‐based feature extractor for classification task. The optimal feature set obtained by the best performing CNN Model, that is, VGG 19 has been subjected to correlation‐based feature extraction and ML‐based classifiers including (i) adaptive neuro fuzzy classifier‐linguistic hedges (ii) principal component analysis‐ support vector machine classifier and (iii) GA‐SVM classifier to yield an optimal hybrid computer aided diagnostic (CAD) system design. It is found that hybrid CAD system using VGG19 as feature extractor and ANFC‐LH as classifier yields 96% the highest classification accuracy. The other performance parameters yield values, that is 96% sensitivity, 96% specificity, 96% F‐score, 96% precision and 92% MCC, which indicates a best prediction of binary classification. The test images which were misclassified by these hybrid CAD system designs were analysed subjectively by experienced participating radiologist. The results obtained by present work suggests that proposed hybrid CAD system with VGG19 Network model acting as feature extractor and ANFC‐LH classifier can be employed for the differential diagnosis of benign as well as malignant mammographic masses using SFM images in a routine clinical setting.