Abstract
The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer in VGG assumes that the training samples belong to exactly only one class, which is not valid in a real situation, such as in medical image diagnosis. To overcome this situation, SVM is employed instead of the softmax layer in VGG. Data augmentation is also employed as DLA usually requires a large number of samples. VGG model with different SVM kernels is built to classify the mammograms. Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%.
Highlights
Breast cancer is the leading cause of death in India, accountable for 9% of non-communicable diseases [1]
Results show that the Visual Geometric Group (VGG)-Support Vector Machine (SVM) model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%
4 Conclusions This paper presents an intelligent mammogram learning system via Deep Learning Architecture (DLA) and SVM for breast cancer diagnosis
Summary
Breast cancer is the leading cause of death in India, accountable for 9% of non-communicable diseases [1]. Different supervised learning algorithms are developed and used in breast cancer diagnoses such as k-nearest neighbour [2], naive Bayes [3], support vector machine [4,5,6,7], Linear regression [8], artificial neural network [9,10], decision tree [11,12] and extreme learning machine [13] These algorithms require a separate feature extraction module to extract dominant features from the training samples. The state-of-the-art DLAs such as VGG [17], DenseNet [18], AlexNet [19], and GoogleNet [20] have a great achievement in classifying thousands of natural objects These pre-trained models can be effectively analyzed for mammogram classification system using transfer learning approach. To extract the highly dominant features for breast cancer classification, the VGG model is utilized, and SVM is incorporated at the output layer to improve the classification results.
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