Compact single hidden layer feedforward network for mycobacterium tuberculosis detection

• Published in: Proceedings - 2011 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2011
• Main Author: Osman M.K.; Noor M.H.M.; Mashor M.Y.; Jaafar H.
• Format: Conference paper
• Language: English
• Published: 2011
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862113556&doi=10.1109%2fICCSCE.2011.6190565&partnerID=40&md5=08bbaac8449a130d7833179c0ebfa197

Advances in imaging technology and artificial intelligence have greatly enhanced the research and development of computer-aided tuberculosis (TB) diagnosis system. The system aims to assist medical technologist and improve the accuracy of clinical diagnosis. A typical architecture of a computer-aided TB diagnosis system consists of image processing, feature extraction and classification. Finding an effective classifier for the system has been regarded as a critical topic, in order to improve the detection performance and avoid making false decision. In this study, the recent method called compact single hidden layer feedforward neural network (C-SLFN) trained by an improved Extreme Learning Machine (ELM) is evaluated for detecting the TB bacilli. Six affine moment invariants are extracted from segmented tissue slide images and fed into the C-SLFN. The network is trained and classified the input patterns into three classes: TB, overlapped TB and non-TB. Further, the study compares the network performance with a SLFN trained using the standard ELM algorithm. The results obtained from this study suggested that the standard ELM still outperformed the C-SLFN in term of classification accuracy. The standard ELM, however requires a large number of hidden nodes compares to the C-SLFN. © 2011 IEEE.