An Efficient End-to-End Deep Neural Network for Interstitial Lung
Disease Recognition and Classification
- URL: http://arxiv.org/abs/2204.09909v1
- Date: Thu, 21 Apr 2022 06:36:10 GMT
- Title: An Efficient End-to-End Deep Neural Network for Interstitial Lung
Disease Recognition and Classification
- Authors: Masum Shah Junayed, Afsana Ahsan Jeny, Md Baharul Islam, Ikhtiar
Ahmed, A F M Shahen Shah
- Abstract summary: This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns.
The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function.
A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model.
- Score: 0.5424799109837065
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The automated Interstitial Lung Diseases (ILDs) classification technique is
essential for assisting clinicians during the diagnosis process. Detecting and
classifying ILDs patterns is a challenging problem. This paper introduces an
end-to-end deep convolution neural network (CNN) for classifying ILDs patterns.
The proposed model comprises four convolutional layers with different kernel
sizes and Rectified Linear Unit (ReLU) activation function, followed by batch
normalization and max-pooling with a size equal to the final feature map size
well as four dense layers. We used the ADAM optimizer to minimize categorical
cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with
five classes is taken to train and assess the proposed model. A comparison
study showed that the presented model outperformed pre-trained CNNs and
five-fold cross-validation on the same dataset. For ILDs pattern
classification, the proposed approach achieved the accuracy scores of 99.09%
and the average F score of 97.9%, outperforming three pre-trained CNNs. These
outcomes show that the proposed model is relatively state-of-the-art in
precision, recall, f score, and accuracy.
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