The application of evolutionary computation towards the characterization and classification of urothelium cell cultures

This thesis presents a novel method for classifying and characterizing urothelial cell cultures. A system of cell tracking employing computer vision techniques was applied to a one day long time-lapse videos of replicate normal human uroepithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS) as inhibitor. Subsequent analysis following feature extraction on both cell culture and single-cell demonstrated the ability of the approach to successfully classify the modulated classes of cells using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the cell class separation. This approach provides a non-biased insight into modulated cell class behaviours

Automated Analysis of Mammograms using Evolutionary Algorithms

Breast cancer is the leading cause of death in women in the western countries. The diagnosis of breast cancer at the earlier stage may be particularly important since it provides early treatment, this will decreases the chance of cancer spreading and increase the survival rates. The hard work is the early detection of any tissues abnormal and confirmation of their cancerous natures. In additionally, finding abnormal on very early stage can also affected by poor quality of image and other problems that might show on a mammogram. Mammograms are high resolution x-rays of the breast that are widely used to screen for cancer in women. This report describes the stages of development of a novel representation of Cartesian Genetic programming as part of a computer aided diagnosis system. Specifically, this work is concerned with automated recognition of microcalcifications, one of the key structures used to identify cancer. Results are presented for the application of the proposed algorithm to a number of mammogram sections taken from the Lawrence Livermore National Laboratory Database. The performance of any algorithm such as evolutionary algorithm is only good as the data it is trained on. More specifically, the class represented in the training data must consist of the true examples or else reliable classifications. Considering the difficulties in obtaining a previously constructed database, there is a new database has been construct to avoiding pitfalls and lead on the novel evolutional algorithm Multi-chromosome Cartesian genetic programming the success on classification of microcalcifications in mammograms

Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection

With the advancement of medical image processing, the area of the healthcare sector has started receiving the benefits of the modern arena of diagnostic tools to identify the diseases effectively. Cancer is one of the dreaded diseases, where success factor of treatment offered by medical sector is still an unsolved problem. Hence, the success factor of the treatment lies in early stage of the disease or timely detection of the disease. This paper discusses about the advancement being made in the medical image processing towards an effective diagnosis of the breast cancer from the mammogram image in radiology. There has been enough research activity with various sorts of advances techniques being implemented in the past decade. The prime contribution of this manuscript is to showcase the advancement of the technology along with illustration of the effectiveness of the existing literatures with respect to research gap

Evolving Artificial Neural Networks using Cartesian Genetic Programming

NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. NeuroEvolution is thought to possess many benefits over traditional training methods including: the ability to train recurrent network structures, the capability to adapt network topology, being able to create heterogeneous networks of arbitrary transfer functions, and allowing application to reinforcement as well as supervised learning tasks. This thesis presents a series of rigorous empirical investigations into many of these perceived advantages of NeuroEvolution. In this work it is demonstrated that the ability to simultaneously adapt network topology along with connection weights represents a significant advantage of many NeuroEvolutionary methods. It is also demonstrated that the ability to create heterogeneous networks comprising a range of transfer functions represents a further significant advantage. This thesis also investigates many potential benefits and drawbacks of NeuroEvolution which have been largely overlooked in the literature. This includes the presence and role of genetic redundancy in NeuroEvolution's search and whether program bloat is a limitation. The investigations presented focus on the use of a recently developed NeuroEvolution method based on Cartesian Genetic Programming. This thesis extends Cartesian Genetic Programming such that it can represent recurrent program structures allowing for the creation of recurrent Artificial Neural Networks. Using this newly developed extension, Recurrent Cartesian Genetic Programming, and its application to Artificial Neural Networks, are demonstrated to be extremely competitive in the domain of series forecasting

The Application of Evolutionary Algorithms to the Classification of Emotion from Facial Expressions

Emotions are an integral part of human daily life as they can influence behaviour. A reliable emotion detection system may help people in varied things, such as social contact, health care and gaming experience. Emotions can often be identified by facial expressions, but this can be difficult to achieve reliably as people are different and a person can mask or supress an expression. Instead of analysis on static image, the computing of the motion of an expression’s occurrence plays more important role for these reasons. The work described in this thesis considers an automated and objective approach to recognition of facial expressions using extracted optical flow, which may be a reliable alternative to human interpretation. The Farneback’s fast estimation has been used for the dense optical flow extraction. Evolutionary algorithms, inspired by Darwinian evolution, have been shown to perform well on complex,nonlinear datasets and are considered for the basis of this automated approach. Specifically, Cartesian Genetic Programming (CGP) is implemented, which can find computer programme that approaches user-defined tasks by the evolution of solutions, and modified to work as a classifier for the analysis of extracted flow data. Its performance compared with Support Vector Machine (SVM), which has been widely used in expression recognition problem, on a range of pre-recorded facial expressions obtained from two separate databases (MMI and FG-NET). CGP was shown flexible to optimise in the experiments: the imbalanced data classification problem is sharply reduced by applying an Area under Curve (AUC) based fitness function. Results presented suggest that CGP is capable to achieve better performance than SVM. An automatic expression recognition system has also been implemented based on the method described in the thesis. The future work is to propose investigation of an ensemble classifier implementing both CGP and SVM

Objective Assessment of Neurological Conditions using Machine Learning

Movement disorders are a subset of neurological conditions that are responsible for a significant decline in the health of the world’s population, having multiple negative impacts on the lives of patients, their families, societies and countries’ economy. Parkinson’s disease (PD), the most common of all movement disorders, remains idiopathic (of unknown cause), is incurable, and without any confirmed pathological marker that can be extracted from living patients. As a degenerative condition, early and accurate diagnosis is critical for effective disease management in order to preserve a good quality of life. It also requires an in-depth understanding of clinical symptoms to differentiate the disease from other movement disorders. Unfortunately, clinical diagnosis of PD and other movement disorders is subject to the subjective interpretation of clinicians, resulting in a high rate of misdiagnosis of up to 25%. However, computerised methods can support clinical diagnosis through objective assessment. The major focus of this study is to investigate the use of machine learning approaches, specifically evolutionary algorithms, to diagnose, differentiate and characterise different movement disorders, namely PD, Huntington disease (HD) and Essential Tremor (ET). In the first study, movement features of three standard motor tasks from Unified Parkinson’s Disease Rating Scale (UPDRS), finger tapping, hand opening-closing and hand pronation-supination, were used to evolve the high-performance classifiers. The results obtained for these conditions are encouraging, showing differences between the groups of healthy controls, PD, HD and ET patients. Findings on the most discriminating features of the best classifiers provide insight into different characteristics of the neurological disorders under consideration. The same algorithm has also been applied in the second study on Dystonia patients. A differential classification between Organic Dystonia and Functional Dystonia patients is less convincing, but positive enough to recommend future studies

An Approach to Pattern Recognition by Evolutionary Computation

Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems. Among the others, EC–based algorithms have also been used to tackle classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an EC–based framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation. The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature

Genetic Programming based Feature Manipulation for Skin Cancer Image Classification

Skin image classification involves the development of computational methods for solving problems such as cancer detection in lesion images, and their use for biomedical research and clinical care. Such methods aim at extracting relevant information or knowledge from skin images that can significantly assist in the early detection of disease. Skin images are enormous, and come with various artifacts that hinder effective feature extraction leading to inaccurate classification. Feature selection and feature construction can significantly reduce the amount of data while improving classification performance by selecting prominent features and constructing high-level features. Existing approaches mostly rely on expert intervention and follow multiple stages for pre-processing, feature extraction, and classification, which decreases the reliability, and increases the computational complexity. Since good generalization accuracy is not always the primary objective, clinicians are also interested in analyzing specific features such as pigment network, streaks, and blobs responsible for developing the disease; interpretable methods are favored. In Evolutionary Computation, Genetic Programming (GP) can automatically evolve an interpretable model and address the curse of dimensionality (through feature selection and construction). GP has been successfully applied to many areas, but its potential for feature selection, feature construction, and classification in skin images has not been thoroughly investigated. The overall goal of this thesis is to develop a new GP approach to skin image classification by utilizing GP to evolve programs that are capable of automatically selecting prominent image features, constructing new high level features, interpreting useful image features which can help dermatologist to diagnose a type of cancer, and are robust to processing skin images captured from specialized instruments and standard cameras. This thesis focuses on utilizing a wide range of texture, color, frequency-based, local, and global image properties at the terminal nodes of GP to classify skin cancer images from multiple modalities effectively. This thesis develops new two-stage GP methods using embedded and wrapper feature selection and construction approaches to automatically generating a feature vector of selected and constructed features for classification. The results show that wrapper approach outperforms the embedded approach, the existing baseline GP and other machine learning methods, but the embedded approach is faster than the wrapper approach. This thesis develops a multi-tree GP based embedded feature selection approach for melanoma detection using domain specific and domain independent features. It explores suitable crossover and mutation operators to evolve GP classifiers effectively and further extends this approach using a weighted fitness function. The results show that these multi-tree approaches outperformed single tree GP and other classification methods. They identify that a specific feature extraction method extracts most suitable features for particular images taken from a specific optical instrument. This thesis develops the first GP method utilizing frequency-based wavelet features, where the wrapper based feature selection and construction methods automatically evolve useful constructed features to improve the classification performance. The results show the evidence of successful feature construction by significantly outperforming existing GP approaches, state-of-the-art CNN, and other classification methods. This thesis develops a GP approach to multiple feature construction for ensemble learning in classification. The results show that the ensemble method outperformed existing GP approaches, state-of-the-art skin image classification, and commonly used ensemble methods. Further analysis of the evolved constructed features identified important image features that can potentially help the dermatologist identify further medical procedures in real-world situations