A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

Abstract

Breast cancer is one of the deadly cancer among the female population and still a developing area of research in the field of medical imaging. The fatality rate is more in patients who are not early diagnosed and are given delayed treatment. Hence, researchers are keeping their lot of efforts in developing breast cancer detection systems that could provide accurate diagnosis in the initial stages which are relied on medical imaging. Deep learning is offering key solutions to overcome many image classification tasks. Although deep learning techniques have extended their root to many medical fields even it suffers from the problem of lack of sufficient data. Convolutional neural networks are more preferred for medical image classification tasks. In this paper, we propose a transfer learning method that overcomes the issue of insufficient data. We use a familiar pre-trained network VGG-16 (Visual Geometric Group) + with logistic regression as a binary classifier. Since hyper-parameters of every CNN have a closer impact on the performance of the entire deep learning model, our method focuses on optimizing hyper-parameters using particle swarm optimization which is a bio-inspired algorithm. The proposed model performs the classification of breast histopathology images into benign and malignant images and produce better results. We use BreakHis Dataset to test our method and achieve an accuracy of around 96.9%. The experimental results show that this study has improved performance metrics when compared to other transfer learning methods.