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              <text>Identification of interstitial lung diseases using deep learning</text>
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              <text>Classification; Deep learning; Medical image analysis; Neural network</text>
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              <text>The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet.  2020 Institute of Advanced Engineering and Science. All rights reserved.</text>
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              <text>Raju N.; Anita H.B.; Augustine P.</text>
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              <text>International Journal of Electrical and Computer Engineering, Vol-10, No. 6, pp. 6283-6291.</text>
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              <text>Institute of Advanced Engineering and Science</text>
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              <text>2020-01-01</text>
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              <text>&lt;a href="https://doi.org/10.11591/IJECE.V10I6.PP6283-6291" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.11591/IJECE.V10I6.PP6283-6291&lt;/a&gt;
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              <text>All Open Access; Gold Open Access; Green Open Access</text>
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              <text>ISSN: 20888708</text>
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              <text>Online</text>
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              <text>Raju N., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Augustine P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India</text>
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