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Multi-class classification of breast cancer abnormality using transfer learning

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Abstract

According to the survey of World Health Organization (WHO), in 2020 there are 2.3 million women found with breast cancer and 685,000 deaths in world wide. 81% women get affected with cancer over the age of 50 at the time of detection. Breast cancer is the world’s number 2 cancer and number 1 cancer in India and 66% survival rate in India is very low if compare to 90% in U.S and 90.2% in Australia. However, treatment for this cancer has possibility of 90% or more. So that, it need to be detect the cancer at very early stage to overcome the death rate. Main objective of this research to design a Breast Cancer diagnose system using image processing and deep learning which can be helpful for radiologist and physician for treating the diagnosis. Basically, Deep learning is a fast-developing fashion inside the health care enterprise and facilitates medical experts to examine records and pick out trends. And image processing plays vital role for enhancing the quality of image by removing noise which is very helpful for better abnormality classification. Now a days Convolution Neural Networks (CNNs) are very popular due to its better performance. In this work, we have used transfer learning with pre-trained VGG16 model. At initial testing stage, the model shows the over-fitting and after that performance improved. Hence we achieved better results by using this approach on DDSM and UPMC data-sets for breast cancer classification. Classifier classify the images into four classes as asymmetry, calcification, carcinoma and mass. Initially 2276 images were taken and divided into 80%-20% ratio. The accuracy achieved by this approach varied from 92% to 95%. We have also used transfer learning with VGG19 and ResNet50 for comparison and found VGG16 much powerful among them. We found, transfer learning with VGG16 giving better results on DDSM and UPMC data-sets. However, breast cancer divided into different categories according to its type, grade or stage of abnormalities, severity of cancer, aggressiveness of cancerous cells, presence/absence of gene etc. Hence classification can be done basis on other types of abnormalities.

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Author notes

  1. Neha Rani, Deepak Kumar Gupta and Samayveer Singh contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science & Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India

    Neha Rani, Deepak Kumar Gupta & Samayveer Singh

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  1. Neha Rani
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  2. Deepak Kumar Gupta
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Rani, N., Gupta, D.K. & Singh, S. Multi-class classification of breast cancer abnormality using transfer learning. Multimed Tools Appl 83, 75085–75100 (2024). https://doi.org/10.1007/s11042-023-17832-2

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