Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
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DOI：
10.1016/j.asoc.2019.105765
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发表时间：
2019-12-01
影响因子：
8.7
通讯作者：
Cibuk, Musa
中科院分区：
文献类型：
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作者：
Budak, Umit;Comert, Zafer;Cibuk, Musa
乳腺癌是世界范围内成年女性最常见的癌症类型之一。许多BC患者由于诊断和治疗的延迟而面临不可逆转的情况，甚至死亡。因此，近年来，基于病理乳腺图像的早期乳腺癌诊断系统一直很受欢迎。提出了一种基于完全卷积网络(FCN)和双向长短期记忆(BiLSTM)的端到端BC检测模型。FCN被用作高级特征提取的编码器。FCN的输出由平坦层转换为一维序列，并馈入BiLSTM的输入。这种方法确保了高分辨率图像被用作模型的直接输入。我们的实验是在BreaKHis数据库上进行的，该数据库可在http://web.inf.ufpr.br/vri/breast-cancer-database.上公开获得为了评价该方法的性能，考虑到五重交叉验证技术，使用了精度度量。结果表明，该方法的性能优于以前报道的结果。(C)2019爱思唯尔B.V.保留所有权利。
Breast cancer (BC) is one of the most frequent types of cancer that adult females suffer from worldwide. Many BC patients face irreversible conditions and even death due to late diagnosis and treatment. Therefore, early BC diagnosis systems based on pathological breast imagery have been in demand in recent years. In this paper, we introduce an end-to-end model based on fully convolutional network (FCN) and bidirectional long short term memory (Bi-LSTM) for BC detection. FCN is used as an encoder for high-level feature extraction. Output of the FCN is turned to a one-dimensional sequence by the flatten layer and fed into the Bi-LSTM's input. This method ensures that high-resolution images are used as direct input to the model. We conducted our experiments on the BreaKHis database, which is publicly available at http://web.inf.ufpr.br/vri/breast-cancer-database. In order to evaluate the performance of the proposed method, the accuracy metric was used by considering the five-fold cross-validation technique. Performance of the proposed method was found to be better than previously reported results. (C) 2019 Elsevier B.V. All rights reserved.