Microfluidic device for novel breast cancer screening by blood test using miRNA beacon probe.

Breast cancer is identified as the highest cause of death in women suffering from cancer. Early diagnosis is the key to increase the survival of breast cancer victims. Molecular diagnosis using biomarkers have advanced much in the recent years. The cost involved in such diagnosis is not affordable for most of the population. The concept being investigated here is to realize a simple diagnosis system for screening cancer by way of a blood test utilizing a miRNA based biomarker with a complementary molecular beacon probe. A microfluidic platform was designed and attached with a fluorescence reader, which is portable and cost effective. Experiments were performed with 51 blood samples of which 30 were healthy and 21 were positive for breast cancer, collected against institutional human ethical clearance, IHEC 16/180-7-9-2016. miRNA 21 was chosen as the biomarker because it is overexpressed 4-fold in the serum of breast cancer patients. This work involved design of an experiment to prove the concept of miRNA over expression followed by detection of miRNA 21 using the microfluidic platform attached with a fluorescence reader and validation of the results using quantitative Real Time Polymerase Chain Reaction (qRT-PCR). The results obtained from the microfluidic device concurred with qRT-PCR results. The device is suitable for point-of-care application in a mass-screening programme. The study also has revealed that the stage of the cancer could be indicated by this test, which will be further useful for deciding a therapeutic regime.

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting.

Cardiac disease classification using heart rate signals.

Heart rate and Heart Rate Variability (HRV) are important measures that reflect the state of the cardiovascular system. HRV analysis has gained prominence in the field of cardiology for detecting cardiac abnormalities. This paper presents the study made on the use of linear (time domain and frequency domain) and nonlinear measures of heart rate variability for accurate classification of certain cardiac diseases. Three different classifiers, viz. Random Forests, Logistic Model Tree and Multilayer Perceptron Neural Network have been used for the classification. Data for use in this work has been obtained from the standard ECG databases in the Physionet website. Classification has been attempted using linear parameters, nonlinear parameters and combined. The classification results indicate that the combination of linear and nonlinear measures is a better indicator of heart diseases than linear or nonlinear measures alone. The results obtained by this study are comparable with those obtained with other techniques cited in the literature.

Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms.

An important approach for describing a region is to quantify its structure content. In this paper, the use of functions for computing texture based on statistical measures is described. Six textural features for mammogram images are defined. The segmentation based on these textures would classify the breast tissue under four categories. The algorithm evaluates the region properties of the mammogram image and thereby would classify the image under four important categories based on the intensity level of histograms. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). The breast tissue classification thus obtained is comparatively better than the other normal methods. The validation of the work has been done by visual inspection of the segmented image by an expert radiologist. This work is a part of developing a computer aided decision (CAD) system for early detection of breast cancer. The classification results agree with the standard specified by the ACR-BIRADS (American College of Radiology-Breat Imaging And Reporting Data Systems). The accuracy of classification has been found to be 80% as per the visual inspection by an expert radiologist.

Computer aided decision system for early detection of breast cancer.

BACKGROUND & OBJECTIVES: Mammography is currently the method of choice for early detection of breast cancer in women. However, the interpretation of mammograms is largely based on radiologist's opinion. In this study an attempt is made to develop an image processing algorithm for the detection of microcalcifications and also a computer based decision system for early detection of breast cancer. The proposed method deals with a novel approach for the development of a computer aided decision (CAD) system for early detection of breast cancer by mammogram image analysis. METHODS: The proposed method employs simple thresholding the region of interest and the use of filters for clear identification of microcalcifications. The method suggested for the detection of microcalcifications from a mammogram image segmentation and analysis was tested over several images taken from mini-MIAS (Mammogram Image Analysis Society, UK) database. The algorithm was implemented using Metlab codes programming and hence can work effectively on a simple personal computer with digital mammogram as stored data for analysis. RESULTS: The algorithm works faster so that any radiologist can take a clear decision about the appearance of microcalcifications by visual inspection of digital mammograms. The performance of the algorithm was tested over several images and the validation of results by visual inspection were done by an expert radiologist. Also, the system has given good detection rate as high as 78 percent. The performance analysis of the CAD algorithm was done by receiver operating characteristics (ROC) plot. INTERPRETATION & CONCLUSION: The CAD system suggested here was capable of detecting microcalcifications with a high detection rate, and thus could be used for early detection of breast cancer.

Detection of breast cancer by mammogram image segmentation.

An important approach for describing a region is to quantify its structure content. In this paper the use of functions for computing texture based on statistical measures is prescribed. MPM (Maximizer of the posterior margins) algorithm is employed. The segmentation based on texture feature would classify the breast tissue under various categories. The algorithm evaluates the region properties of the mammogram image and thereby would classify the image into important segments. Images from mini-MIAS data base (Mammogram Image Analysis Society database (UK)) have been considered to conduct our experiments. The segmentation thus obtained is comparatively better than the other normal methods. The validation of the work has been done by visual inspection of the segmented image by an expert radiologist. This is our basic step for developing a computer aided detection (CAD) system for early detection of breast cancer.

Neural classification of lung sounds using wavelet coefficients.

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.