Aaron Park

Detection of basal cell carcinoma by automatic classification of confocal raman spectra

Raman spectroscopy has strong potential for providing noninvasive dermatological diagnosis of skin cancer. In this study, we investigated various classification methods with confocal Raman spectra for the detection of basal cell carcinoma (BCC), which is one of the most common skin cancer. The methods include maximum a posteriori (MAP) probability, probabilistic neural networks (PNN), k-nearest neighbor (KNN), multilayer perceptron networks (MLP), and support vector machine (SVM). The classification framework consists of preprocessing of Raman spectra, feature extraction, and classification. In the preprocessing step, a simple half Hanning method is adopted to obtain robust features. Classification results involving 216 spectra gave about 97% true classification rate in case of MLP and SVM, which is an evident proof of the effectiveness of confocal Raman spectra for BCC detection. In addition to it, spectral regions, which are important for classification, are examined by sensitivity analysis.

A screening of Alzheimer's disease using basis synthesis by singular value decomposition from Raman spectra of platelet

Abstract In this paper, we proposed a method to screening of Alzheimers disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

Screening of Basal Cell Carcinoma by Automatic Classifiers with an Ambiguous Category

Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability (MAP), multilayer perceptron networks (MLP), and support vector machine (SVM) showed competitive results. Since even the small errors often leads to a fatal result, we investigated the method that reduces classification error perfectly by screening out some ambiguous patterns. Those ambiguous patterns can further be examined by routine biopsy. We incorporated an ambiguous category in MAP, linear classifier using minimum squared error (MSE), and MLP. The experiments involving 216 confocal Raman spectra showed that every methods could perfectly classify basal cell carcinoma (BCC) by screening out some ambiguous patterns. The best results were obtained with MSE. According to the experimental results, MSE gave perfect classification by screening out 8% of test patterns.

Basal Cell Carcinoma Detection by Classification of Confocal Raman Spectra

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half hanning window and dimension reduction with principal components analysis (PCA). The application of the half hanning window deemphasizes the peak near 1650cm −1 and improves classification performance by lowering the false positive ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori (MAP) probability, k-nearest neighbor (KNN), and artificial neural network (ANN) classifier. Classification results with ANN involving 216 confocal Raman spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.

In silico prediction of potential chemical reactions mediated by human enzymes

BackgroundAdministered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms.ResultWe developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition.ConclusionOur model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

Noisy OTDR Data Event Detection Analysis for the Real Time Optical Fiber Link Monitoring

Abstract This paper, proposes a new analysis method for the event detection of an OTDR signal. An OTDR signalwas passed through the Hamming filter to remove the high frequency noise included in the signal. The signal wasthen passed consecutively through a differential filter to detect the events of interest. The terminal position was determined using the fact that there is a large gap between the signal and the trailing noise power beyond the terminal.This study examined the local maxima of the signal up to the terminal position and determined the peak regions.The real events were determined from the peak regions using noise information and peak threshold. Finally, the userevents were found by inspecting the user peaks beyond the terminal position. The events of the OTDR signal withoutusers are located at less than 17m compared to the optical fiber link setup. The events of the JDSU device are locatedless than 25m and their users are less than 5m. For the RadianTech device, the events are detected at less than 19mand the users are found in 5m. The results suggest that the proposed method is sufficiently applicable to an opticalfiber link monitoring system.

A Fast Search Algorithm for Raman Spectrum using Singular Value Decomposition

In this paper, we propose new search algorithms using SVD(Singular Value Decomposition) for fast search of Raman spectrum. In the proposed algorithms, small number of the eigen vectors obtained by SVD are chosen in accordance with their respective significance to achieve computation reduction. By introducing pilot test, we exclude large number of data from search and then, we apply partial distance search(PDS) for further computation reduction. We prepared 14,032 kinds of chemical Raman spectrum as the library for comparisons. Experiments were carried out with 7 methods, that is Full Search, PDS, 1DMPS modified MPS for applying to 1-dimensional space data with PDS(1DMPS+PDS), 1DMPS with PDS by using descending sorted variance of data(1DMPS Sort with Variance+PDS), 250-dimensional components of the SVD with PDS(250SVD+PDS) and proposed algorithms, PSP and PSSP. For exact comparison of computations, we compared the number of multiplications and additions required for each method. According to the experiments, PSSP algorithm shows 64.8% computation reduction when compared with 250SVD+PDS while PSP shows 157% computation reduction.

Estimation of Optimal Mixture Number of GMM for Environmental Sounds Recognition

In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.

Gaussian Mixture Model using Minimum Classification Error for Environmental Sounds Recognition Performance Improvement

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

Application of MAP and MLP Classifier on Raman Spectral Data for Classification of Liver Disease

In this paper, we evaluated the performance of the automatic classifier applied for the discrimination of acute alcoholic liver injury and chronic liver fibrosis. The classifier uses the discriminant peaks of the preprocessed Raman spectrum as a feature set. In preprocessing step, we subtract baseline and apply Savitzky-Golay smoothing filter which is known to be useful at preserving peaks. After identifying discriminant peaks from the spectra, we carried out the classification experiments using MAP and neural networks. According to the experimental results, the classifier shows the promising results to diagnosis alcoholic liver injury and chronic liver fibrosis. Classification results over 80% means that the peaks used as a feature set is useful for diagnosing liver disease.