Penambahbaikan Proses Pengekstrakan Ciri Dan Pengklasifikasian Sel Kanser Pangkal Rahim Untuk Sistem Neuralpap
| dc.contributor.author | Sulaiman, Siti Noraini | |
| dc.date.accessioned | 2019-01-07T03:16:50Z | |
| dc.date.available | 2019-01-07T03:16:50Z | |
| dc.date.issued | 2012-02 | |
| dc.description.abstract | Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research suggests several new image processing algorithms to reduce these constraints. The Fuzzy membership-k-means (FMKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. The Switching Cluster algorithm and De-noising Based Clustering algorithm are proposed to segment noise contaminated cervical cell images. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the FMKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer by NeuralPap system to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system, tested with 799 cervical cells. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/7468 | |
| dc.language.iso | en | en_US |
| dc.subject | Cervix uteri | en_US |
| dc.subject | Cancer | en_US |
| dc.title | Penambahbaikan Proses Pengekstrakan Ciri Dan Pengklasifikasian Sel Kanser Pangkal Rahim Untuk Sistem Neuralpap | en_US |
| dc.type | Thesis | en_US |
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