Sistem diagnosis awal barah pangkal rahim berasaskan rangkaian neural

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Date
2002-08
Authors
Mat Isa, Norashidi
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Abstract
Pap test is an effective screening test for cervical cancer. However, Pap test has three disadvantages namely it does not always produce accurate diagnosis result due to human and technical errors, requires lot of pathologists services and diagnosis result can only be obtained after a long duration. Therefore, the current study focused on developing a diagnosis support system for Pap test. In image processing system, automatic seed based region growing (ASBRG) algorithm is proposed to segment the Pap smear image into nucleus, cytoplasm and background areas. The proposed automatic algorithm produced better segmentation performance as compared to conventional techniques by ensuring complete segmentation process and maintaining the original size and shape of cell structures. The current study also proposed Pap smear image segmentation technique based on moving k-means clustering. The proposed technique was capable to reduce dead cluster, cluster redundancy and trapped cluster in local minima problem. It gave better performance as compared to conventional clustering techniques. The current study also proposed region growing based feature extraction (RGBFE) algorithm to extract size and grey level of nucleus and cytoplasm automatically. The suggested combination of moving k-means and linear contrast was shown to be capable of enhancing the contrast of Pap smear image for easier screening process by pathologists. In proposed diagnosis system, the current study go one step further as compared to previous studies by classifying cervical cell into normal, LSIL and HSIL cell. For that purpose, hierarchical neural network has been proposed. The results. proved that the hierarchical hybrid multilayered perceptron (H2MLP) network produced better diagnostic performance as compared to otter hierarchical, hybrid and conventional neural networks. The current study has also successfully proposed two new analysis called diagnosis confidence level and confidence percentage to give clearer picture on the confidence level for each diagnosis. Based on these researches, an early diagnosis system for cervical cancer based on neural network has been developed by. ...~ om.. bining jhe ima. ge processing system .a nd . diagnosis system. The prop.csed system is called NeuralPap. NeuralPap is an automatic diagnosis system. First, NeuralPap will segment the Pap smear image by using ASBRG algorithm. Then, the cervical cell features will be extracted using RGBFE algorithm. Lastly, by using the extracted features, the diagnosis will be carried out by H2MLP network. For overall diagnostic performance, NeuralPap produced 94.29%, 89.42% and 100.00% of accuracy, sensitivity and specificity respectively. On top of that, NeuralPap also provides supplementary facilities which are image processing, diagnostic performance comparison using various neural networks and general information about neural network and cervical cancer.
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Barah pangkal rahim , Rangkaian neural
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