Cite

[1] van Ginneken B, Hogeweg L, Prokop M. Computer-aided diagnosis in chest radiography: beyond nodules. Eur J Radiol. 2009;72(2): 226-230.10.1016/j.ejrad.2009.05.06119604661Search in Google Scholar

[2] Bogoni L, Ko JP, Alpert J, et al. Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams. J Digit Imaging. 2012;25(6):771-781.10.1007/s10278-012-9496-0349116222710985Search in Google Scholar

[3] Lodwick GS. Computer-aided diagnosis in radiology: A research plan. Invest Radiol. 1966;1(1), 72-80.10.1097/00004424-196601000-000325910559Search in Google Scholar

[4] Campadelli P, Casiraghi E, Valentini G. Lung nodules detection and classification. ICIP205. IEEE International Conference on Image Processing 2005. 2005: I-1117-1120.10.1109/ICIP.2005.1529951Search in Google Scholar

[5] Hardie RC, Rogers S, Wilson T, et al. Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs. Med Image Anal. 2008;12(3):240-258.10.1016/j.media.2007.10.00418178123Search in Google Scholar

[6] Yeung DS, Ng WWY, Wang D, et al. Localized generalization error model and its application to architecture selection for radial basis function neural network. IEEE Trans Neural Netw. 2007;18(5):1294-1305.10.1109/TNN.2007.89405818220181Search in Google Scholar

[7] Hamidzadeh J, Monsefi R, Sadoghi Yazdi H. DDC: distance-based decision classifier. Neural Comput Applic. 2012;21(7):1697-1707.10.1007/s00521-011-0762-8Search in Google Scholar

[8] Al Gindi, A., Rashed, E., & Sami, M. (2014). Development and Evaluation of a Computer-Aided Diagnostic Algorithm for Lung Nodule Characterization and Classification in Chest Radiographs using Multiscale Wavelet Transform.Journal of American Science, 10(2).Search in Google Scholar

[9] Zhou T, Lu H, Zhang J, et al. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. Biomed Res Int. 2016.10.1155/2016/8052436504610027722173Search in Google Scholar

[10] Froz BR, de Carvalho Filho AO, Silva AC, et al. Lung nodule classification using artificial crawlers, directional texture and support vector machine. Expert Syst Appl. 2017;69:176-188.10.1016/j.eswa.2016.10.039Search in Google Scholar

[11] Ben Hassen D, Taleb H, Yaacoub IB, et al. Classification of chest lesions with using fuzzy c-means algorithm and support vector machines. In: International Joint Conference SOCO’13-CISIS’13-ICEUTE’13 (pp. 319-328). Springer International Publishing. 2014.10.1007/978-3-319-01854-6_33Search in Google Scholar

[12] Ben Hassen D, Taleb H, Ben Yaacoub I, et al. A fuzzy approach to chest radiography segmentation involving spatial relations. IJCA Special Issue on Novel Aspects of Digital imaging Applications (DIA). 2011;(1):40-47.Search in Google Scholar

[13] Ben Hassen D, Taleb, H. Automatic detection of lesions in lung regions that are segmented using spatial relations. Clin Imaging. 2013;37(3):498-503.10.1016/j.clinimag.2012.07.01023601768Search in Google Scholar

[14] van Ginneken B, Stegmann MB, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med Image Anal. 2006;10(1):19-40.10.1016/j.media.2005.02.00215919232Search in Google Scholar

[15] Porebski A. Sélection d’attributs de texture couleur pour la classification d’images. Application à l’identification de défauts sur les décors verriers imprimés par sérigraphie [Doctoral dissertation]. Université Lille; 2009.Search in Google Scholar

[16] Jain AK, Duin RPW, Mao J. Statistical pattern recognition: A review. IEEE Trans Pattern Analysis and Machine Intelligence. 2000;22(1):4-37.10.1109/34.824819Search in Google Scholar

[17] Kong H, Wang L, Teoh EK, et al. Generalized 2D principal component analysis for face image representation and recognition. Neural Networks. 2005;18(5):585-594.10.1016/j.neunet.2005.06.04116112550Search in Google Scholar

[18] Metz CE. ROC methodology in radiologic imaging. Invest Radiol. 1986;21(9):720-733.10.1097/00004424-198609000-000093095258Search in Google Scholar

eISSN:
1898-0309
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Medicine, Biomedical Engineering, Physics, Technical and Applied Physics, Medical Physics