A biological continuum based approach for efficient clinical classification

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

AN IMPROVED GREY WOLF OPTIMIZATION-BASED LEARNING OF ARTIFICIAL NEURAL NETWORK FOR MEDICAL DATA CLASSIFICATION

Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson's diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an ANN.

Image quilting and wavelet fusion for creation of synthetic microscopy nuclei images

BACKGROUND AND OBJECTIVE

In this study a texture simulation methodology is proposed for composing synthetic tissue microscopy images that could serve as a quantitative gold standard for the evaluation of the reliability, accuracy and performance of segmentation algorithms in computer-aided diagnosis.

METHODS

A library of background and nuclei regions was generated using pre-segmented Haematoxylin and Eosin images of brain tumours. Background image samples were used as input to an image quilting algorithm that produced the synthetic background image. Randomly selected pre-segmented nuclei were randomly fused on the synthetic background using a wavelet-based fusion approach. To investigate whether the produced synthetic images are meaningful and similar to real world images, two different tests were performed, one qualitative by an experienced histopathologist and one quantitative using the normalized mutual information and the Kullback-Leibler tests. To illustrate the challenges that synthetic images may pose to object recognition algorithms, two segmentation methodologies were utilized for nuclei detection, one based on the Otsu thresholding and another based on the seeded region growing approach.

RESULTS

Results showed a satisfactory to good resemblance of the synthetic with the real world images according to both qualitative and quantitative tests. The segmentation accuracy was slightly higher for the seeded region growing algorithm (87.2%) than the Otsu's algorithm (86.3%).

CONCLUSIONS

Since we know the exact coordinates of the regions of interest within the synthesised images, these images could then serve as a 'gold standard' for evaluation of segmentation algorithms in computer-aided diagnosis in tissue microscopy.