Abstract
Breast cancer is one of the most commonly occurring types of cancer and the treatment administered to a subject is dependent on the grade or type of the lesion. In this manuscript, we make use of an ensemble of convolutional neural networks (CNN) to classify histology images as Normal, In-situ, Benign or Invasive. The performance of CNN is dependent on the network architecture, number of training instances and also on the data normalization scheme. However, there exists neither a single architecture nor a pre-processing regime that promises best performance. For the reason stated above, we use 3 CNNs trained on different pre-processing regimes to form an ensemble. On the held out test data (n = 40), the proposed scheme achieved an accuracy of 97.5%. On the challenge data (n = 100) provided by the organizers, the proposed technique achieved an accuracy of 87% and was jointly adjudged as the top performing algorithm for the task of classification of breast cancer from histology images.
All authors have contributed equally.
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Chennamsetty, S.S., Safwan, M., Alex, V. (2018). Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_91
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DOI: https://doi.org/10.1007/978-3-319-93000-8_91
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