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Convolutional Neura...
Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images
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- Agarwala, Sunita (författare)
- Natl Inst Technol Durgapur, Comp Sci & Engn, Durgapur, India.
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- Kumar, Abhishek (författare)
- Univ Hyderabad, Sch Comp & Informat Sci, Hyderabad, India.
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- Nandi, Debashis (författare)
- Natl Inst Technol Durgapur, Comp Sci & Engn, Durgapur, India.
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- Dhara, Ashis Kumar (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
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- Sadhu, Anup (författare)
- Med Coll Kolkata, Kolkata, India.
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- Thakur, Sumitra Basu (författare)
- Med Coll Kolkata, Kolkata, India.
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- Bhadra, Ashok Kumar (författare)
- Med Coll Kolkata, Kolkata, India.
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Natl Inst Technol Durgapur, Comp Sci & Engn, Durgapur, India Univ Hyderabad, Sch Comp & Informat Sci, Hyderabad, India. (creator_code:org_t)
- 2018-08-21
- 2018
- Engelska.
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Ingår i: Medical Image Understanding and Analysis. - Cham : Springer Nature. - 9783319959214 - 9783319959207 ; , s. 12-22
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- Lung field segmentation is the first step towards the development of any computer aided diagnosis (CAD) system for interstitial lung diseases (ILD) observed in chest high resolution computed tomography (HRCT) images. If the segmentation is not done efficiently it will compromise the accuracy of CAD system. In this paper, a deep learning-based method is proposed to localize several interstitial lung disease patterns (ILD) in HRCT images without performing lung field segmentation. In this paper, localization of several ILD patterns is performed in image slice. The pretrained models of ZF and VGG networks were fine-tuned in order to localize ILD patterns using Faster R-CNN framework. The three most difficult ILD patterns consolidation, emphysema, and fibrosis have been used for this study and the accuracy of the method has been evaluated in terms of mean average precision (mAP) and free receiver operating characteristic (FROC) curve. The model achieved mAP value of 75% and 83% on ZF and VGG networks, respectively. The result obtained shows the effectiveness of the method in the localization of different ILD patterns.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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