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Classification Of Breast Cancer Histology Images Using ALEXNET

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Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

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Training a deep convolutional neural network from scratch requires massive amount of data and significant computational power. However, to collect a large amount of data in medical field is costly and difficult, but this can be solved by some clever tricks such as mirroring, rotating and fine tuning pre-trained neural networks. In this paper, we fine tune a deep convolutional neural network (ALEXNET) by changing and inserting input layer convolutional layers and fully connected layer. Experimental results show that our method achieves a patch and image-wise accuracy of 75.73% and 81.25% respectively on the validation set and image-wise accuracy of 57% on the ICIAR-2018 breast cancer challenge hidden test set.

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Correspondence to Wajahat Nawaz .

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Nawaz, W., Ahmed, S., Tahir, A., Khan, H.A. (2018). Classification Of Breast Cancer Histology Images Using ALEXNET. 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.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

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