Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification

Marinakis, Yannis; Dounias, Georgios; Jantzen, Jan

doi:10.1016/j.compbiomed.2008.11.006

Cited by 132 publications

(90 citation statements)

References 13 publications

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“…However, detection of the cytoplasm regions is also crucial because cytoplasm features have been shown to be very useful for the identification of abnormal cells [15]. Even so, these nuclei-specific methods do not necessarily generalize well for the detection of cytoplasms that create an increased difficulty due to additional gradient content and local variations.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang and Liu [23] performed pixel-based classification using 4000 multispectral features with SVM-based feature selection. Marinakis et al [15] used 20 features computed from both nucleus and cytoplasm regions using feature selection with a genetic algorithm and a nearest neighbor classifier. They also considered a more detailed seven-class problem but observed a decrease in accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised segmentation and classification of cervical cell images

Genctav

Aksoy

Önder

2012

Pattern Recognition

226

176

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a b s t r a c tThe Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, and a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, and then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, and overlapping cells.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised segmentation and classification of cervical cell images

Genctav

Aksoy

Önder

2012

Pattern Recognition

226

176

View full text Add to dashboard Cite

show abstract

“…In the testing phase, the testing dataset is given to the proposed technique to find the cancers type in smear images and the obtained results are evaluated through evaluation metrics namely, sensitivity, specificity and accuracy [16], it is given by (eqn. [10][11][12] …”

Section: Experimental Results and Comparative Analysismentioning

confidence: 99%

“…Marinakis et al [11] proposed a metaheuristic approach to classify the Pap smear cells. Uniquely described twenty features are extracted from each cell image and classified as normal and abnormal type.…”

Section: Related Workmentioning

confidence: 99%

Multi Class Cervical Cancer Classification by using ERSTCM, EMSD & CFE methods based Texture Features and Fuzzy Logic based Hybrid Kernel Support Vector Machine Classifier

Athinarayanan¹,

Srinath²,

Kavitha³

2017

IOSR JCE

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“…There are many methodologies focused on FS, seen from different point of views [7][8]. The association rules technique [9], a hill climbing algorithm [10], particle swarm optimization [11], and genetic algorithms [12] are among the mostly used tools for FS in computer-aided medical diagnosis.This study proposes a FS procedure, based on a previous regression approach, using the decision classes as the output of the linear multiple regression model, and the features as predictors. A common approach is based on the exploratory examination of the correlation matrix involving all variables, and expecting to highlight the underlying correlation between features and the decision class.…”

mentioning

confidence: 99%

Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence

Belciug

Serbănescu

2015

ACTA Universitatis Cibiniensis

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3 4 T 2 T Abstract:2 T 3 7 T 1 T 3 7 T Feature selection is considered a key factor in classifications/decision problems. It is currently used in designing intelligent decision systems to choose the best features which allow the best performance. This paper proposes a regression-based approach to select the most important predictors to significantly increase the classification performance. Application to breast cancer detection and recurrence using publically available datasets proved the efficiency of this technique.1 T 2 T Key words:2 T 3 7 T classification, feature selection, regression, neural networks, breast cancer detection and recurrence IntroductionRecent advances in Artificial Intelligence and Data Mining allowed researchers to develop the socalled intelligent decision models, smartly behaving like human beings. Most of them are based on a standalone machine-learning algorithm, such as artificial neural networks (NNs), genetic algorithms (GAs), support vector machines (SVMs), swarm intelligence (SI), random forests (RF), etc.[1]The use of the intelligent decision models in medical research is a current practice, supporting the doctors' decision in a wide range of specialities. Artificial neural networks are applied to predict the severe acute pancreatitis at admission to hospital [2]. A support vector machines approach has been used in epilepsy, for the seizure prediction with spectral power of EEG [3]. Support vector machines along with linear kernel and classification trees have been used for early diagnosis of Alzheimer-type dementia [4]. A competitive/collaborative neural computing system has been considered to early detect the pancreatic cancer [5]. A hybrid neural network-genetic algorithm has been applied for the breast cancer detection and recurrence [6].The direct use of a medical database in decision/classification purposes, without a previous analysis and pre-processing step, is often counterproductive. It is noteworthy that even the best classifier will perform poorly if the features are not chosen well. The feature selection (FS) represents the way of choosing the most relevant attributes from a dataset for improving the performance of the models used in the decision/classification process. There are many methodologies focused on FS, seen from different point of views [7][8]. The association rules technique [9], a hill climbing algorithm [10], particle swarm optimization [11], and genetic algorithms [12] are among the mostly used tools for FS in computer-aided medical diagnosis.This study proposes a FS procedure, based on a previous regression approach, using the decision classes as the output of the linear multiple regression model, and the features as predictors. A common approach is based on the exploratory examination of the correlation matrix involving all variables, and expecting to highlight the underlying correlation between features and the decision class. However, in such an attempt, there is no "automatic" way to weed out the "false" correlations. Different from this approach, th...

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Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification

Cited by 132 publications

References 13 publications

Unsupervised segmentation and classification of cervical cell images

Unsupervised segmentation and classification of cervical cell images

Multi Class Cervical Cancer Classification by using ERSTCM, EMSD & CFE methods based Texture Features and Fuzzy Logic based Hybrid Kernel Support Vector Machine Classifier

Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence

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