Addressing architectural distortion in mammogram using AlexNet and support vector machine

Abstract

Objective: To address the architectural distortion (AD) which is an irregularity in the parenchymal pattern of breast. The nature of AD is extremely complex; still, the study is very much essential because AD is viewed as a primitive sign of breast cancer. In this study, a new convolutional neural network (CNN) based system is developed that performs classification of AD distorted mammograms and other mammograms. Methods: In the first part, mammograms undergo pre-processing and image augmentation techniques. In the other half, learned and handcrafted features are retrieved. The AlexNet Pretrained CNN is utilized for extraction of learned features. The support vector machine (SVM) validates the existence of AD. For improved classification, the scheme is tested for various conditions. Results: A sophisticated CNN based system is developed for stepwise analysis of AD. The maximum accuracy, sensitivity and specificity yielded as 92%, 81.50% and 90.83% respectively. The results outperform the conventional methods. Conclusion: Based on the overall study, it is recommended that a combination of CNN pre-trained network and support vector machine is a good option for identification of AD. The study will motivate researchers to find improved methods of high performance. Further, it will also help the radiologists. Significance: The AD can develop up to two years before the growth of any anomaly. The proposed system will play an essential role in the detection of early manifestations of breast cancer. The system will aid society to go for better treatment options for women all over the world and curtail the mortality rate.

Keywords

• Mammogram
• Architectural distortion
• Convolutional neural networks
• AlexNet
• Support vector machine
• Augmentation