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

Niwas, S. Issac; Palanisamy, P.; Chibbar, Rajni; Zhang, Wenjun

doi:10.1007/s10916-011-9788-9

Cited by 30 publications

(3 citation statements)

References 70 publications

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“…Two of them are ANFIS and SVMs which are effective methods for classification and machine learning systems such as face detection, speech recognition, pattern recognition and lots of biomedical applications [36–41]. Recently, multiclass SVMs have been very popular and successful for different classification issues [42, 43].…”

Section: Introductionmentioning

confidence: 99%

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

Ankishan

Yılmaz

2013

Computational and Mathematical Methods in Medicine

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Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS.

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Section: Introductionmentioning

confidence: 99%

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

Ankishan

Yılmaz

2013

Computational and Mathematical Methods in Medicine

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“…Then a BP neural network and radial basis function neural network were used to recognize the reconstructed images, with accuracy rates of 59.0% and 70.4%, respectively (Kaymak et al, 2017). The Issac Niwas team dissected the stained tissue, HSV color features and Log‐Gabor wavelet transform features were input into the SVM for classification and recognition, and a 93.2% recognition rate was obtained (Niwas et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of hepatocytes and hepatoma cells using atomic force microscopy and machine learning algorithms

Zeng

Liu

Wang

et al. 2023

Microscopy Res & Technique

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Hepatocellular carcinoma is a high‐risk malignant tumor. Hepatoma cells are transformed from normal cells and have unique surface nanofeatures in addition to the characteristics of the original cells. In this paper, atomic force microscopy was used to extract the three‐dimensional morphology and mechanical information of HL‐7702 human hepatocytes and SMMC‐7721 and HepG2 hepatoma cells in culture, such as the elastic modulus and viscoelasticity. The characteristics of different cells were compared and analyzed. Finally, the cell morphology and mechanics information were used for training machine learning algorithms. With the trained model, the detection of cells was realized. The classification accuracy was as high as 94.54%, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.99. Thus, hepatocytes and hepatoma cells were accurately identified and assessed. We also compared the classification effects of other machine learning algorithms, such as support vector machine and logistic regression. Our method extracts cellular nanofeatures directly from the surface of cells of unknown type for cell classification. Compared with microscope image‐based analysis and other methods, this approach can avoid the misjudgment that may occur when different doctors have different levels of experience. Thus, the proposed method provides an objective basis for the early diagnosis of hepatocellular carcinoma. Research Highlights The 3D appearance and mechanical characteristics of hepatocellular carcinoma cells are very similar to those of hepatocytes. Application of atomic force microscopy with machine learning algorithm. Collect the data set of nano‐characteristic parameters of the cell. The machine learning algorithms is trained by data set, and its classification effect is better than that of a single nano‐parameter.

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“…Issac Niwas et al (2012[67]) have used Log-Gabor wavelet transform base decomposition method for histopathological images on HSV (Hue, Saturation, Value) colour space. The accuracy obtained by Least squares Support Vector Machine (LS-SVM) in this study was 98.3 %.…”

Section: Introductionmentioning

confidence: 99%

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian

Mashohor

Mahmud

et al. 2017

EXCLI Journal; 16:Doc113; ISSN 1611-2156

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

Cited by 30 publications

References 70 publications

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

Detection and classification of hepatocytes and hepatoma cells using atomic force microscopy and machine learning algorithms

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

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