An Ensemble Based Classification Approach for Medical Images

Ensemble classification is a classifier applied to improve the performance of the single classifiers by fusing the output of the individual classifier models. Research in ensemble methods has largely revolved around designing ensemble consisting of single classifier models. The main discovery of the ensemble classifier, constructed by ensemble machine algorithms is to perform much better accuracy than the single classifiers. The ability to perform classification accuracy in single classifier models has been increased but in single classifier accuracy of classification is less. The difficulty arises because the algorithms for single classifier algorithm have designed with less capacitance. Now-a-days more researchers are applying the ensemble learning algorithm for classification to obtain high accuracy in an effectual manner

In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM).

We present our latest work on in vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). The in vivo skin capacitive images were taken by a capacitance based fingerprint sensor, the skin capacitive images were then analysed by GLCM. Four different GLCM feature vectors, angular second moment (ASM), entropy (ENT), contrast (CON) and correlation (COR), are selected to describe the skin texture. The results show that angular second moment increases as age increases, and entropy decreases as age increases. The results also suggest that the angular second moment values and the entropy values reflect more about the skin texture, whilst the contrast values and the correlation values reflect more about the topically applied solvents. The overall results shows that the GLCM is an effective way to extract and analyse the skin texture information, which can potentially be a valuable reference for evaluating effects of medical and cosmetic treatments

Skin Hydration and Solvent Penetration Measurements by Opto-thermal Radiometry, AquaFlux and Fingerprint Sensor

The aim of this study is to develop new data analysis techniques and new measurement methodologies for skin hydration and solvent penetration measurements by using Opto-Thermal Transient Emission Radiometry (OTTER), AquaFlux and capacitive contact imaging based on Fingerprint sensor, three novel technologies developed by our research group. This research work is divided into three aspects: the theoretical work, the experimental work and the portable opto-thermal radiometry hardware design work. In the theoretical work, a) an effective image retrieval method based on Gabor wavelet transform has been developed, the results show that it is particularly useful for retrieving the grayscale capacitive skin images; b) an algorithm based on Grey Level Co-occurrence Matrix (GLCM) has been developed to analyze the grayscale capacitive skin images; c) a comparison study of Gabor wavelet transform, Grey level co-occurrence matrix (GLCM) and Principal Component Analysis (PCA) has been conducted in order to understand the performance of each algorithm, and to find out which algorithm is suitable for what type of images. In the opto-thermal radiometry hardware design work, a new, low cost, portable opto-thermal radiometry instrument, based on a broadband Infrared emitter and a room temperature PbS detector, has been designed and developed. The results show that it can work on any unprepared sample surfaces. In the experimental work, various in-vivo and in-vitro measurements were performed in order to study skin hydration and solvent penetration through skin and membranes. The results show that, combined with tape stripping, capacitive skin imaging can be a powerful tool for skin hydration, skin texture and solvent penetration measurements. The effect of three different parameters of Fingerprint sensor and its detection depth are also studied. The outcomes of this work have provided a better understanding for skin hydration and solvent penetration measurements and have generated several publications

Multiscale image representation in deep learning

Deep learning is a very popular field of research which can input a variety of data types [1, 16, 30]. It is a subfield of machine learning consisting of mostly neural networks. A challenge which is very commonly met in the training of neural networks, especially when working with images is the vast amount of data required. Because of this various data augmentation techniques have been proposed to create more data at low cost while keeping the labelling of the data accurate [65]. When a model is trained on images these augmentations include rotating, flipping and cropping the images [21]. An added advantage of data augmentation is that it makes the model more robust to rotation and transformation of an object in an image [65]. In this mini-dissertation we investigate the use of the Discrete Pulse Transform [54, 2] decomposition algorithm and its Discrete Pulse Vectors (DPV) [17] as data augmentation for image classification in deep learning. The DPVs is used to extract features from the image. A convolutional neural network is trained on the original and augmented images and a comparison made to a convolutional neural network only trained on the unaugmented images. The purpose of the models implemented is to correctly classify an image as either a cat or dog. The training and testing accuracy of the two approaches are similar. The loss of the model using the proposed data augmentation is improved. When making use of probabilities predicted by the model and determining a custom cut off to classify an image into one of the two classes, the model trained on using the proposed augmentation outperforms the model trained without the proposed data augmentation.Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2020.The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.StatisticsMSc (Advanced Data Analytics)Unrestricte