@inproceedings{6268846d2a0b4c1da547d18f09cfe64a,
title = "Classification Of Breast Cancer Histology Images Using ALEXNET",
abstract = "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.",
keywords = "Carcinoma cancer, Convolution neural network, Deep learning, Pathologists, Transfer learning",
author = "Wajahat Nawaz and Sagheer Ahmed and Ali Tahir and Khan, {Hassan Aqeel}",
year = "2018",
month = jun,
day = "6",
doi = "10.1007/978-3-319-93000-8_99",
language = "English",
isbn = "9783319929996",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "869--876",
editor = "{ter Haar Romeny}, Bart and Fakhri Karray and Aurelio Campilho",
booktitle = "Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings",
address = "Germany",
note = "15th International Conference on Image Analysis and Recognition, ICIAR 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
}