Proceedings Article10.1109/ISDA.2010.5687018
A genetic algorithm-neural network approach for Mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue slide images
Muhammad Khusairi Osman,Fadzil Ahmad,Z. Saad,Mohd Yusoff Mashor,Hasnan Jaafar +4 more
- 01 Nov 2010
- pp 1229-1234
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TL;DR: Experimental results demonstrated that the GA-NN approach able to produce better performance with fewer input features compared to the standard NN approach.
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Abstract: This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image segmentation, feature extraction and identification. It uses Ziehl-Neelsen stained tissue slides images which are acquired using a digital camera attached to a light microscope for diagnosis. To separate the tubercle bacilli from its background, moving k-mean clustering that uses C-Y colour information is applied. Then, seven Hu's moment invariants are extracted as features to represent the bacilli. Finally, based on the input features, a GA-NN approach is used to classify into two classes: ‘true TB’ and ‘possible TB’. In this study, genetic algorithm (GA) is applied to select significant input features for neural network (NN). Experimental results demonstrated that the GA-NN approach able to produce better performance with fewer input features compared to the standard NN approach.
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A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches
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References
Visual pattern recognition by moment invariants
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review.
Karen R Steingart,Megan Henry,Vivienne Ng,Philip C. Hopewell,Andrew Ramsay,Jane Cunningham,Richard Urbanczik,Mark D. Perkins,Mohamed Abdel Aziz,Madhukar Pai +9 more
TL;DR: The results suggest that, overall, fluorescence microscopy is more sensitive than conventional microscopy, and has similar specificity, and there is insufficient evidence to determine the value of fluorescence microscopeopy in HIV-infected individuals.
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Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears
Rethabile Khutlang,Sriram Krishnan,Ronald Dendere,Andrew Whitelaw,K. Veropoulos,G. Learmonth,Tania S. Douglas +6 more
- 01 Jul 2010
TL;DR: The authors' results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.
127
•Journal Article
Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains.
TL;DR: This work demonstrates proof of principle of an innovative computational algorithm that successfully recognizes Ziehl-Neelsen (ZN) stained acid-fast bacilli (AFB) in digital images and facilitates electronic diagnosis of TB, permitting wider application in developing countries where fluorescent microscopy is currently inaccessible and unaffordable.
Image processing and neural computing used in the diagnosis of tuberculosis
K. Veropoulos,Colin Campbell,G. Learmonth +2 more
- 20 Oct 1998
TL;DR: The study presented in this paper indicates that machine-assisted diagnosis of tuberculosis is certainly feasible and should be more accurate due to the higher number of view-fields processed.
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