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The analysis of blood cells in microscope images can provide useful information concerning the health of patients; however, manual classification of blood cells is time-consuming and susceptible to error due to the different morphological features of the cells. Therefore, a fast and automated method for identifying the different blood cells is required. In this paper, we investigate the use of different neural network models for the purpose of cell identification. The neural models are based on the back propagation learning algorithm and differ in design according to the way data features are extracted from the cell microscopic images. Three different topologies of neural networks are investigated, and a comparison between these models is drawn. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes, and orientation. [ABSTRACT FROM AUTHOR]

Emotional processes and responses are biological and psychological processes in humans. The modelling of these biological processes in machines is considered a challenging and often controversial task in the field of bio-inspired computing. This is because of the complexity of emotional processes and rarity of available emotional computational models. However, research into possible mechanisms that allow emotional phenomena to influence cognitive and behavioural processes and how they can be implemented in robots has recently been gaining momentum with successful results in modelling emotions within systems. The motive for this trend is that future machines and robots will be capable of making better decisions if their cognition is enhanced by emotions. We present in this paper an emotional neural network based on an improved emotional back propagation (EmBP) learning algorithm. The modified algorithm, namely the iEmBP learning algorithm, models within its configuration three emotional responses: anxiety, confidence and impression. The proposed iEmBP-based emotional neural network will be implemented to a blood cell type identification problem, and its performance will be compared to two other neural networks. Experimental results show that the iEmBP-based emotional neural network has improved performance when compared to the EmBP-based network, and significantly outperforms the conventional back propagation (BP)-based neural networks in recognition results and time cost. [ABSTRACT FROM AUTHOR]

The thought that machines could have emotions has so far been unacceptable by humans. However, few decades ago the idea of machines with intelligence was also ridiculed, but today not only we have developed intelligent machines, but we are also exploring many areas for their application with great success. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model some emotions in machine learning. This paper presents four emotional neural network models (EmNNs) that are based on the emotional back propagation (EmBP) learning algorithm. The EmNNs have emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The four EmNN models differ in their topologies as a result of different input data coding methods. The performance of the EmNN models when applied to a blood cell type identification problem will be evaluated. Experimental results show that the additional emotional parameters and weights in all four models improve the identification rate as well as the classification time. [ABSTRACT FROM AUTHOR]

The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time. [ABSTRACT FROM AUTHOR]

Abstract: The analysis of blood cells in microscope images can provide useful information concerning the health of patients. There are three major blood cell types, namely, erythrocytes (red), leukocytes (white), and platelets. Manual classification is time consuming and susceptible to error due to the different morphological features of the cells. This paper presents an intelligent system that simulates a human visual inspection and classification of the three blood cell types. The proposed system comprises two phases: The image preprocessing phase where blood cell features are extracted via global pattern averaging, and the neural network arbitration phase where training is the first and then classification is carried out. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes and orientation, thus providing a fast, simple and efficient rotational and scale invariant blood cell identification system which can be used in automating laboratory reporting. [Copyright &y& Elsevier]

Classification of blood cell types can be time consuming and susceptible to error due to the different morphological features of the cells. This paper presents a blood cell identification system that simulates a human visual inspection and identification of the three blood cell types. The proposed system uses global pattern averaging to extract cell features, and a neural network to classify the cell type. Two neural networks are investigated and a comparison between these networks is drawn. Experimental results suggest that the proposed system provides fast, simple and efficient identification which can be used in automating laboratory reporting. [ABSTRACT FROM AUTHOR]