Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Computational intelligence image processing for precision farming on-site nitrogen analysis in plants

PhD ThesisNitrogen is one of the macronutrients which is essentially required by plants. To support the precision farming, it is important to analyse nitrogen status in plants in order to prevent excessive fertilisation as well as to reduce production costs. Image-based analysis has been widely utilised to estimate nitrogen content in plants. Such research, however, is commonly conducted in a controlled environment with artificial lighting systems. This thesis proposes three novel computational intelligence systems to evaluate nitrogen status in wheat plants by analysing plant images captured on field and are subject to variation in lighting conditions. In the first proposed method, a fusion of regularised neural networks (NN) has been employed to normalise plant images based on the RGB colour of the 24-patch Macbeth colour checker. The colour normalisation results are then optimised using genetic algorithm (GA). The regularised neural network has also been effectively utilised to distinguish wheat leaves from other unwanted parts. This method gives improved results compared to the Otsu algorithm. Furthermore, several neural networks with different number of hidden layer nodes are combined using committee machines and optimised by GA to estimate nitrogen content. In the second proposed method, the utilisation of regularised NN has been replaced by deep sparse extreme learning machine (DSELM). In general the utilisation of DSELM in the three research steps is as effective as that of the developed regularised NN as proposed in the first method. However, the learning speed of DSELM is extremely faster than the regularised NN and the standard backpropagation multilayer perceptron (MLP). In the third proposed method, a novel approach has been developed to fine tune the colour normalisation based on the nutrient estimation errors and analyse the effect of genetic algorithm based global optimisation on the nitrogen estimation results. In this method, an ensemble of deep learning MLP (DL-MLP) has been employed in the three research steps, i.e. colour normalisation, image segmentation and nitrogen estimation. The performance of the three proposed methods has been compared with the intrusive SPAD meter and the results show that all the proposed methods are superior to the SPAD based estimation. The nutrient estimation errors of the proposed methods are less than 3%, while the error using the renowned SPAD meter method is 8.48%. As a comparison, nitrogen prediction using other methods, i.e. Kawashima greenness index () and PCA-based greenness index () are also calculated. The prediction errors by means of and methods are 9.84% and 9.20%, respectively.Indonesia Ministry of Research, Technology and Higher Education and Jenderal Soedirman Univerist