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- W2772067798 abstract "This proposed method will give a suggestion for Tuberculosis (TB) diagnosing using Artificial Neural Networks (ANN). Since diagnostic imaging techniques such as x-rays (Radiographs), Magnetic Resonance Imaging (MRI), Computed Tomography (CT) are available, X-ray techniques is widely preferred for edging the image of TB affected area in the chest region. This method is preferred due to its fastness and easy imaging and also inexpensive nature. The X-ray imaging couldn't show a small cancer and blood clot in chest, it works fine to categorize TB. But the accuracy of disease diagnostics depends on the ability of practitioner owing to lack of automatic approach. Hence this paper focuses to present an automated approach to recognize TB in conventional posteroanterior chest Radiographs. This includes 3 stages (i) segmentation of lung region from conventional posteroanterior chest radiographs. (ii) Extract a set of features (iii) classification section to identify the presence and absence of TB. Initially Graph-cut method is used as segmentation algorithm to extract the Region of Interest (ROI) from the input then classification will be done by Artificial Neural Network (ANN) in combination with Genetic Algorithm (GA). This ANN utilizes Levenberg-Marquardt Algorithm (LMA) to train the neuron." @default.
- W2772067798 created "2017-12-22" @default.
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- W2772067798 date "2017-04-01" @default.
- W2772067798 modified "2023-09-27" @default.
- W2772067798 title "Tuberculosis malady recognition in chest radiographs via artificial neural networks" @default.
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- W2772067798 doi "https://doi.org/10.1109/iceice.2017.8191949" @default.
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