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Convolutional neural network long short-term memory (CNN + LSTM) for histopathology cancer image classification

Zainudin, Zanariah and Shamsuddin, Siti Mariyam and Hasan, Shafaatunnur (2020) Convolutional neural network long short-term memory (CNN + LSTM) for histopathology cancer image classification. In: 2nd International Conference on Machine Intelligence and Signal Processing, MISP 2019, 7 - 10 September 2019, Allahabad, India.

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Official URL: http://dx.doi.org/10.1007/978-981-15-1366-4_19

Abstract

Deep learning algorithm such as Convolutional Neural Networks (CNN) is popular in image recognition,object recognition, scene recognition and face recognition. Compared to traditional method in machine learning, Convolutional Neural Network (CNN) will give more efficient results. This is due to Convolutional Neural Network (CNN) capabilities in finding the strong feature while training the image. In this experiment, we compared the Convolutional Neural Network (CNN) algorithm with the popular machine learning algorithm basic Artificial Neural Network (ANN). The result showed some improvement when using Convolutional Neural Network Long Short-Term Memory (CNN + LSTM) compared to the multi-layer perceptron (MLP). The performance of the algorithm has been evaluated based on the quality metric known as loss rate and classification accuracy.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Artificial Neural Network, Histopathology Cancer Image
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:92265
Deposited By: Widya Wahid
Deposited On:28 Sep 2021 07:34
Last Modified:28 Sep 2021 07:34

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