Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network

The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image diagnosis of lung cancer. The deep feedback GMDH-type neural network can identified very complex nonlinear systems using heuristic self-organization method which is a type of evolutionary computation. The deep neural network architectures are organized so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network. It is shown that the deep feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of lung cancer because deep neural network architectures are automatically organized using only input and output data

Medical Image Analysis of Brain X-ray CT Images By Deep GMDH-Type Neural Network

The deep Group Method of Data Handling (GMDH)-type neural network is applied to the medical image analysis of brain X-ray CT image. In this algorithm, the deep neural network architectures which have many hidden layers and fit the complexity of the nonlinear systems, are automatically organized using the heuristic self-organization method so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The learning algorithm is the principal component-regression analysis and the accurate and stable predicted values are obtained. The recognition results show that the deep GMDH-type neural network algorithm is useful for the medical image analysis of brain X-ray CT images

Combining group method of data handling models using artificial bee colony algorithm for time series forecasting

Time series forecasting which uses models to predict future values based on some historical data is an important area of forecasting, and has gained the attention of researchers from various related fields of study. In line with its popularity, various models have been introduced for producing accurate time series forecasts. However, to produce an accurate forecast is not an easy feat especially when dealing with nonlinear data due to the abstract nature of the data. In this study, a model for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and Group Method of Data Handling (GMDH) models with variant transfer functions, namely polynomial, sigmoid, radial basis function and tangent was developed. Initially, in this research, the GMDH models were used to forecast the time series data followed by each forecast that was combined using ABC. Then, the ABC produced the weight for each forecast before aggregating the forecasts. To evaluate the performance of the developed GMDH-ABC model, input data on tourism arrivals (Singapore and Indonesia) and airline passengers’ data were processed using the model to produce reliable forecast on the time series data. To validate the evaluation, the performance of the model was compared against benchmark models such as the individual GMDH models, Artificial Neural Network (ANN) model and combined GMDH using simple averaging (GMDH-SA) model. Experimental results showed that the GMDH-ABC model had the highest accuracy compared to the other models, where it managed to reduce the Root Mean Square Error (RMSE) of the conventional GMDH model by 15.78% for Singapore data, 28.2% for Indonesia data and 30.89% for airline data. As a conclusion, these results demonstrated the reliability of the GMDH-ABC model in time series forecasting, and its superiority when compared to the other existing models

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically