eprintid: 7878 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/78/78 datestamp: 2023-11-09 16:19:43 lastmod: 2023-11-09 16:19:43 status_changed: 2023-11-09 16:10:38 type: article metadata_visibility: show creators_name: Khan, S.A. creators_name: Yong, S.-P. title: An evaluation of convolutional neural nets for medical image anatomy classification ispublished: pub keywords: Artificial intelligence; Computer aided diagnosis; Diagnosis; Learning systems; Medical imaging; Object recognition; Signal processing, Anatomical structures; Anatomy classifications; Automatic classification; Computer-aided detection and diagnosis; Deep learning; Natural images, Image classification note: cited By 8; Conference of International Conference on Machine Learning and Signal Processing, MALSIP 2015 ; Conference Date: 12 June 2015 Through 14 June 2015; Conference Code:176019 abstract: Classification of the anatomical structures is an important precondition for several computer aided detection and diagnosis systems. Attaining extraordinary precision for automatic classification is a stimulating job because of vast amount of variation in the anatomical structures. Current trend in object recognition is driven by â��Deep learningâ�� methods that are outperforming the contemporary methods in classification of images. Till now these â��Deep learningâ�� methods have been applied on natural images. In this study, we compare the performance of three main Deep learning architectures i.e. LeNet, AlexNet, GoogLeNet on medical imaging data containing five anatomical structures for anatomic specific classification. © Springer International Publishing Switzerland 2016. date: 2016 publisher: Springer Verlag official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975889824&doi=10.1007%2f978-3-319-32213-1_26&partnerID=40&md5=e0aee8136e3f43b73b90d966fa82d687 id_number: 10.1007/978-3-319-32213-1₂₆ full_text_status: none publication: Lecture Notes in Electrical Engineering volume: 387 pagerange: 293-303 refereed: TRUE isbn: 9783319322124 issn: 18761100 citation: Khan, S.A. and Yong, S.-P. (2016) An evaluation of convolutional neural nets for medical image anatomy classification. Lecture Notes in Electrical Engineering, 387. pp. 293-303. ISSN 18761100