Browsing by Author "Gwetu, Mandlenkosi Victor."

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Forest image classification based on deep learning and ontologies.
(2024) Kwenda, Clopas.; Gwetu, Mandlenkosi Victor.; Fonou-Dombeu, Jean Vincent.
Forests contribute abundantly to nature’s natural resources and they significantly contribute to a wide range of environmental, socio-cultural, and economic benefits. Classifications of forest vegetation offer a practical method for categorising information about patterns of forest vegetation. This information is required to successfully plan for land use, map landscapes, and preserve natural habitats. Remote sensing technology has provided high spatio-temporal resolution images with many spectral bands that make conducting research in forestry easy. In that regard, artificial intelligence technologies assess forest damage. The field of remote sensing research is constantly adapting to leverage newly developed computational algorithms and increased computing power. Both the theory and the practice of remote sensing have significantly changed as a result of recent technological advancements, such as the creation of new sensors and improvements in data accessibility. Data-driven methods, including supervised classifiers (such as Random Forests) and deep learning classifiers, are gaining much importance in processing big earth observation data due to their accuracy in creating observable images. Though deep learning models produce satisfactory results, researchers find it difficult to understand how they make predictions because they are regarded as black-box in nature, owing to their complicated network structures. However, when inductive inference from data learning is taken into consideration, data-driven methods are less efficient in working with symbolic information. In data-driven techniques, the specialized knowledge that environmental scientists use to evaluate images obtained through remote sensing is typically disregarded. This limitation presents a significant obstacle for end users of Earth Observation applications who are accustomed to working with symbolic information, such as ecologists, agronomists, and other related professionals. This study advocates for the incorporation of ontologies in forest image classification owing to their ability in representing domain expert knowledge. The future of remote sensing science should be supported by knowledge representation techniques such as ontologies. The study presents a methodological framework that integrates deep learning techniques and ontologies with the aim of enhancing domain expert confidence as well as increasing the accuracy of forest image classification. In addressing this challenge, this study followed the following systematic steps (i) A critical review of existing methods for forest image classification (ii) A critical analysis of appropriate methods for forest image classification (iii) Development of the state-of-the-art model for forest image segmentation (iv) Design of a hybrid model of deep learning and machine learning model for forest image classification (v) A state-of-the-art ontological framework for forest image classification. The ontological framework was flexible to capture the expression of the domain expert knowledge. The ontological state-of-the-art model performed well as it achieved a classification accuracy of 96%, with a Root Mean Square Error of 0.532. The model can also be used in the fruit industry and supermarkets to classify fruits into their respective categories. It can also be potentially used to classify trees with respect to their species. As a way of enhancing confidence in deep learning models by domain experts, the study recommended the adoption of explainable artificial intelligence (XAI) methods because they unpack the process by which deep learning models reach their decision. The study also recommended the adoption of high-resolution networks (HRNets) as an alternative to traditional deep learning models, because they can convert low-resolution representation to high-resolution and have efficient block structures developed according to new standards and they are excellent at being used for feature extraction.
A patch-based convolutional neural network for localized MRI brain segmentation.
(2020) Vambe, Trevor Constantine.; Viriri, Serestina.; Gwetu, Mandlenkosi Victor.
Accurate segmentation of the brain is an important prerequisite for effective diagnosis, treatment planning, and patient monitoring. The use of manual Magnetic Resonance Imaging (MRI) segmentation in treating brain medical conditions is slowly being phased out in favour of fully-automated and semi-automated segmentation algorithms, which are more efficient and objective. Manual segmentation has, however, remained the gold standard for supervised training in image segmentation. The advent of deep learning ushered in a new era in image segmentation, object detection, and image classification. The convolutional neural network has contributed the most to the success of deep learning models. Also, the availability of increased training data when using Patch Based Segmentation (PBS) has facilitated improved neural network performance. On the other hand, even though deep learning models have achieved successful results, they still suffer from over-segmentation and under-segmentation due to several reasons, including visually unclear object boundaries. Even though there have been significant improvements, there is still room for better results as all proposed algorithms still fall short of 100% accuracy rate. In the present study, experiments were carried out to improve the performance of neural network models used in previous studies. The revised algorithm was then used for segmenting the brain into three regions of interest: White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). Particular emphasis was placed on localized component-based segmentation because both disease diagnosis and treatment planning require localized information, and there is a need to improve the local segmentation results, especially for small components. In the evaluation of the segmentation results, several metrics indicated the effectiveness of the localized approach. The localized segmentation resulted in the accuracy, recall, precision, null-error, false-positive rate, true-positive and F1- score increasing by 1.08%, 2.52%, 5.43%, 16.79%, -8.94%, 8.94%, 3.39% respectively. Also, when the algorithm was compared against state of the art algorithms, the proposed algorithm had an average predictive accuracy of 94.56% while the next best algorithm had an accuracy of 90.83%.
Retinal blood vessel segmentation using random forest Gabor feature selection and automatic thresholding.
(2019) Gwetu, Mandlenkosi Victor.; Tapamo, Jules-Raymond.; Viriri, Serestina.
Successful computer aided diagnosis of ocular diseases is normally dependent on the accurate detection of components such as blood vessels, optic disk, fovea and microaneurysms. The properties of these components can be indicative of the presence and/or severity of pathology. Since most prevalent forms of ocular diseases emanate from vascular disorders, it is expected that accurate detection of blood vessels is essential for ocular diagnosis. In this research work, we investigate several opportunities for improvement of retinal blood vessel segmentation with the hope that they will ultimately lead to improvement in the diagnosis of vascular related ocular diseases. We complement existing work in this domain by introducing new Gabor lter features and selecting the most e ective of these using Random Forests feature selection. The actual segmentation of blood vessels is then done using an improved automatic thresholding scheme based on the preferred Gabor feature. We propose Random Forest (RF) feature ranking algorithms that demonstrate reliable feature set partitions over several University of California, Irvine (UCI) datasets. To circumvent instances of unreliable rankings, we also propose feature rank and RF strength correlation as an alternative indicator. Of the four proposed Gabor features, the maximum magnitude response is con rmed as the most e ective, as is the general trend in previous literature. The proposed Selective Valley Emphasis thresholding technique achieves identical segmentation results to the legacy approach while improving on computational e ciency. Sensitivity and speci city outcomes of up to 76.8% and 97.9% as well as 78.8% and 97.8% are achieved on the DRIVE and STARE datasets, respectively.
Solar flare recurrence prediction & visual recognition.
(2024) Mngomezulu, Mangaliso Moses.; Gwetu, Mandlenkosi Victor.; Fonou-Dombeu, Jean Vincent.
Solar flares are intense outbursts of radiation observable in the photosphere. The radiation flux is measured in W/m2. Solar flares can kill astronauts, disrupt electrical power grids, and interrupt satellite-dependent technologies. They threaten human survival and the efficiency of technology. The reliability of solar flare prediction models is often undermined by the stochastic nature of solar flare occurrence as shown in previous studies. The Geostationary Operational Environmental Satellite (GOES) system classifies solar flares based on their radiation flux. This study investigated how Recurrent Neural Network (RNN) models compare to their ensembles when predicting flares that emit at least 10−6W/m2 of radiation flux, known as ≥C class flares. A Long-Short Term Memory (LSTM) and Simple RNN homogeneous ensemble achieved a similar performance with a tied True Skill Statistic (TSS) score of 70 ± 1.5%. Calibration curves showed that ensembles are more reliable. The balanced accuracies of the Simple RNN Ensemble and LSTM are both 85% with f1-scores of 79% and 77% respectively. Furthermore, this study proposed a framework that shows how objective function reparameterization can be used to improve binary (≥C or