Intelligent classification system for cancer data based on artificial neural network

Isa, Nor Ashidi Mat; Hamid, Nadira; Sakim, Harsa Amylia Mat; Mashor, Mohd Yusoff; Zamli, Kamal Z.

doi:10.1109/iccis.2004.1460411

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…As established in the NeuralPap system, the neural network of the type H 2 MLP [17] has been used to perform the classification based on good performance of the network. However, in the NeuralPap system, the RBF [30] and HRBF [31,32] neural networks have been prepared as the support facilities. Therefore, in this research, some modification has been done which seek to improve both the networks.…”

Section: Methodsmentioning

confidence: 99%

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Sulaiman¹,

Isa²,

Othman³

et al. 2015

Procedia Computer Science

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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 proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. 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 AFKM 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 to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.

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Section: Methodsmentioning

confidence: 99%

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Sulaiman¹,

Isa²,

Othman³

et al. 2015

Procedia Computer Science

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An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features

et al. 2011

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Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

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The classification of breast cancer with Machine Learning Techniques

Kolay

Erdoğmuş

2016

2016 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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ÖzetçeBu çalışmada UCI(University of California-Irvine) Machine Learning Laboratuvarından alınan göğüs kanseri dataları kullanılarak, makine öğrenmesi teknikleri ile göğüs kanserinin sınıflandırılmasına çalışılmıştır. Bu amaçla, göğüs kanseri dataları kullanılarak, K-means kümeleme uzaklık ölçümlerinin kümeleme başarısı Matlab©'da analiz edilmiştir. Daha sonra, hiçbir ön işleme yapmadan, Weka© Veri Madenciliği yazılımı kullanılarak, UCI'den alınan göğüs kanseri dataları, çeşitli makine öğrenme teknikleri ile WEKA'da sınıflandırılmıştır. Sonuç olarak kümeleme uzaklık ölçütlerinin başarıyı yaklaşık olarak %12 değiştirdiği ve sınıflandırma başarısının yöntemlere göre %45-%79 arasında değiştiği görülmüştür. AbstractIn this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab© has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software©. As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from %45 to %79, according to the methods.

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Intelligent classification system for cancer data based on artificial neural network

Cited by 4 publications

References 7 publications

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features

The classification of breast cancer with Machine Learning Techniques

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