Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents

Linked Data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and facilitate the discovery of information. Most archival records have digital representations of physical artifacts in the form of scanned images that are non-machine-readable. Optical Character Recognition (OCR) recognizes text in images and translates it into machine-encoded text. This paper evaluates the impact of image processing methods and parameter tuning in OCR applied to typewritten cultural heritage documents. The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) to tune the methods' parameters. Evaluation results show that parameterization by digital representation typology benefits the performance of image pre-processing algorithms in OCR. Furthermore, our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results. In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the best-performing algorithms for the theatre plays' covers, letters, and overall dataset, respectively, and should be applied before OCR to improve its performance.Comment: 25 pages, 4 figure

AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

Deep learning algorithms have the potential to automate the examination of medical images obtained in clinical practice. Using digitized medical images, convolution neural networks (CNNs) have demonstrated their ability and promise to discriminate among different image classes. As an initial step towards explainability in clinical diagnosis, deep learning models must be exceedingly precise, offering a measure of uncertainty for their predictions. Such uncertainty-aware models can help medical professionals in detecting complicated and corrupted samples for re-annotation or exclusion. This paper proposes a new model and data-agnostic mechanism, called Actionable Uncertainty Quantification Optimization (AUQantO) to improve the performance of deep learning architectures for medical image classification. This is achieved by optimizing the hyperparameters of the proposed entropy-based and Monte Carlo (MC) dropout uncertainty quantification techniques escorted by single- and multi-objective optimization methods, abstaining from the classification of images with a high level of uncertainty. This helps in improving the overall accuracy and reliability of deep learning models. To support the above claim, AUQantO has been validated with four deep learning architectures on four medical image datasets and using various performance metric measures such as precision, recall, Area Under the Receiver Operating Characteristic (ROC) Curve score (AUC), and accuracy. The study demonstrated notable enhancements in deep learning performance, with average accuracy improvements of 1.76% and 2.02% for breast cancer histology and 5.67% and 4.24% for skin cancer datasets, utilizing two uncertainty quantification techniques, and AUQantO further improved accuracy by 1.41% and 1.31% for brain tumor and 4.73% and 1.83% for chest cancer datasets while allowing exclusion of images based on confidence levels

Medical Image Classification using Deep Learning Techniques and Uncertainty Quantification

The emergence of medical image analysis using deep learning techniques has introduced multiple challenges in terms of developing robust and trustworthy systems for automated grading and diagnosis. Several works have been presented to improve classification performance. However, these methods lack the diversity of capturing different levels of contextual information among image regions, strategies to present diversity in learning by using ensemble-based techniques, or uncertainty measures for predictions generated from automated systems. Consequently, the presented methods provide sub-optimal results which is not enough for clinical practice. To enhance classification performance and introduce trustworthiness, deep learning techniques and uncertainty quantification methods are required to provide diversity in contextual learning and the initial stage of explainability, respectively. This thesis aims to explore and develop novel deep learning techniques escorted by uncertainty quantification for developing actionable automated grading and diagnosis systems. More specifically, the thesis provides the following three main contributions. First, it introduces a novel entropy-based elastic ensemble of Deep Convolutional Neural Networks (DCNNs) architecture termed as 3E-Net for classifying grades of invasive breast carcinoma microscopic images. 3E-Net is based on a patch-wise network for feature extraction and image-wise networks for final image classification and uses an elastic ensemble based on Shannon Entropy as an uncertainty quantification method for measuring the level of randomness in image predictions. As the second contribution, the thesis presents a novel multi-level context and uncertainty-aware deep learning architecture named MCUa for the classification of breast cancer microscopic images. MCUa consists of multiple feature extractors and multi-level context-aware models in a dynamic ensemble fashion to learn the spatial dependencies among image patches and enhance the learning diversity. Also, the architecture uses Monte Carlo (MC) dropout for measuring the uncertainty of image predictions and deciding whether an input image is accurate based on the generated uncertainty score. The third contribution of the thesis introduces a novel model agnostic method (AUQantO) that establishes an actionable strategy for optimising uncertainty quantification for deep learning architectures. AUQantO method works on optimising a hyperparameter threshold, which is compared against uncertainty scores from Shannon entropy and MC-dropout. The optimal threshold is achieved based on single- and multi-objective functions which are optimised using multiple optimisation methods. A comprehensive set of experiments have been conducted using multiple medical imaging datasets and multiple novel evaluation metrics to prove the effectiveness of our three contributions to clinical practice. First, 3E-Net versions achieved an accuracy of 96.15% and 99.50% on invasive breast carcinoma dataset. The second contribution, MCUa, achieved an accuracy of 98.11% on Breast cancer histology images dataset. Lastly, AUQantO showed significant improvements in performance of the state-of-the-art deep learning models with an average accuracy improvement of 1.76% and 2.02% on Breast cancer histology images dataset and an average accuracy improvement of 5.67% and 4.24% on Skin cancer dataset using two uncertainty quantification techniques. AUQantO demonstrated the ability to generate the optimal number of excluded images in a particular dataset

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization

In this paper, we propose particle swarm optimization (PSO)-enhanced ensemble deep neural networks and hybrid clustering models for skin lesion segmentation. A PSO variant is proposed, which embeds diverse search actions including simulated annealing, levy flight, helix behavior, modified PSO, and differential evolution operations with spiral search coefficients. These search actions work in a cascade manner to not only equip each individual with different search operations throughout the search process but also assign distinctive search actions to different particles simultaneously in every single iteration. The proposed PSO variant is used to optimize the learning hyper-parameters of convolutional neural networks (CNNs) and the cluster centroids of classical Fuzzy C-Means clustering respectively to overcome performance barriers. Ensemble deep networks and hybrid clustering models are subsequently constructed based on the optimized CNN and hybrid clustering segmenters for lesion segmentation. We evaluate the proposed ensemble models using three skin lesion databases, i.e., PH2, ISIC 2017, and Dermofit Image Library, and a blood cancer data set, i.e., ALL-IDB2. The empirical results indicate that our models outperform other hybrid ensemble clustering models combined with advanced PSO variants, as well as state-of-the-art deep networks in the literature for diverse challenging image segmentation tasks

Fuzzy systems and unsupervised computing: exploration of applications in biology

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics. The theory of fuzzy systems is concerned with formulating decision problems in data sets that are ill-defined. It supports the transfer from a subjective human classification to a numerical scale. In this manner it affords the testing of hypothesis and separation of the classes in the data. We first formulate problems in terms of a fuzzy system and then develop and test algorithms in terms of their performance with data from the domain of the life-sciences. From the results and the performance, we will learn about the usefulness of fuzzy systems for the field, as well as the applicability to the kind of problems and practicality for the computation itself. Computer Systems, Imagery and Medi

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions