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Comparison of Breast Cancer Multi-class Classification Accuracy Based on Inception and InceptionResNet Architecture

  • Madhuvanti MuralikrishnanEmail author
  • R. Anitha
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Breast Cancer is the tumor that occurs most commonly in women and follows lung cancer in the most common cancers. Mortality rates caused by cancer can be reduced if early detection and treatment mechanisms are instituted. With recent advances in Deep Learning and Computer Vision, its application in diagnosis through pattern recognition is fast emerging. This paper compares the classification performance of two convolutional neural network models into benign and malignant subclasses for a breast cancer histopathological dataset. The first is built on Inception v3 architecture while the second contains residual connections in the Inception network called InceptionResNet v2. Performance has been enhanced by augmenting the data. Time taken to train and computational cost have been reduced through transfer learning. The results show accuracy from 85.9% to 91.3% on Inception v3 model. Inception Resnet v2 model performs better than Inception v3 with accuracies ranging from 89.8% to 94.6%.

Keywords

Breast cancer Convolutional neural networks Image classification 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSri Venkateswara College of EngineeringSriperumbudurIndia

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