Department of Statistics and Actuarial Science

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Browsing Department of Statistics and Actuarial Science by Subject "Artificial intelligence -- Medical applications -- South Africa"

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    Biomedical image analysis of brain tumours through the use of artificial intelligence
    (Stellenbosch : Stellenbosch University, 2022-04) Di Santolo, Claudia; Muller, C. J. B.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: Cancer is one of the leading causes of morbidity and mortality on a global scale. More specifically, cancer of the brain, which is one of the rarest forms. One of the major challenges is that of timely diagnoses. In the ongoing fight against cancer early and accurate detection in combination with effective treatment strategy planning remains one of the best tools for improved patient outcomes and success. Emphasis has been placed on the identification and classification of brain lesions in patients - that is, either the absence or presence of brain tumours. In the case of malignant brain tumours it is critical to classify patients into either high-grade or low-grade brain lesion groups: different gradings of brain tumours have different prognoses, thus different survival rates. The growth in the availability and accessibility of big data due to digitisation has led individuals in the area of bioinformatics in both academia and industry to apply and evaluate artificial intelligence techniques. However, one of the most important challenges, not only in the field of bioinformatics but also in other realms, is transforming the raw data into valuable insights and knowledge. In this research thesis artificial intelligence techniques that can detect vital and fundamental underlying patterns in the data are reviewed. The models may provide significant predictive performance to assist with decision making. Much artificial intelligence has been applied to brain tumour classification and segmentation in the research literature. However, in this study the theoretical background of two more traditional machine learning methods, namely š‘˜-nearest neighbours and support vector machines, is discussed. In recent years, deep learning (artificial neural networks) has gained prominence due to its ability to handle copious amounts of data. The specialised version of the artificial neural network that is reviewed is convolutional neural networks. The rationale behind this particular technique is that it is applied to visual imagery. In addition to making use of the convolutional neural network architecture, the study reviews the training of neural networks that involves the use of optimisation techniques, considered to be one of the most difficult parts. Utilising only one learning algorithm (optimisation technique) in the architecture of convolutional neural network models for classification tasks may be regarded as insufficient unless there is strong support in the design of the analysis for using a particular technique. Nine state-of-the-art optimisation techniques formed part of a comparative study to determine if there was any improvement in the classification and segmentation of high-grade or low-grade brain tumours. These machine learning and deep learning techniques have proved to be successful in image classification and - more relevant to this research ā€“ brain tumours. To supplement the theoretical knowledge, these artificial intelligence methodologies (models) are applied through the exploration of magnetic resonance imaging scans of brain lesions.