| Summary: | 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.
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