The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

Extrinsic Factors Influencing the Effective Use of Security Awareness Guidelines: A Comparative Study between a Bank and a Telecommunications Company

Recently, the telecommunication and banking industries, regarded as key infrastructures of a country’s economy, are experiencing a rapid transformation driven by changing consumer behaviors, increased competitive environment and new innovations, for example mobile technology. Thus, the purpose of this study is to investigate the influence of extrinsic factors on the behavioral decisions of users to effectively use a security awareness program. This study is quantitative in nature and explores the relationship between effective information use and other variables namely; management support, reward, punishment, social pressure, information quality and attitude. The results of the empirical testing demonstrate that information quality and attitude of employees are relevant factors towards using a security awareness program. Our results also show that reward and threat of punishment are less relevant factors

Malaria Surveillance System Using Social Media

Social media, for example, Twitter has increasingly provided opportunities for massive data collection of topical issues affecting today’s society. Opinions and data in public health issues are very prevalent on Twitter and provide an invaluable source of interesting information that can be mined for decision making in public health organizations. This paper discusses the existing malaria surveillance system and proposes a malaria surveillance system (MSS) that leverages social media with a view to enhancing decision making by public health professionals. The MSS system comprises of a data collection module, analysis engine, and a metrics module. The practical contribution of this research is the construction of a conceptual architecture for the MSS

EFFECTIVENESS OF TRANSFER LEARNING ON MEDICAL IMAGE CLASSIFICATION USING CHEST-XRAY 14 DATASET

https://scholar.dsu.edu/research-symposium/1024/thumbnail.jp

Topical Mining of malaria Using Social Media. A Text Mining Approach

Malaria is a life-threatening parasitic disease, common in subtropical and tropical climates caused by mosquitoes. Each year, several hundred thousand of people die from malaria infections. However, with the rapid growth, popularity and global reach of social media usage, a myriad of opportunities arises for extracting opinions and discourses on various topics and issues. This research examines the public discourse, trends and emergent themes surrounding malaria discussion. We query Twitter corpus leveraging text mining algorithms to extract and analyze topical themes. Further, to investigate these dynamics, we use Crimson social media analytics software to analyze topical emergent themes and monitor malaria trends. The findings reveal the discovery of pertinent topics and themes regarding malaria discourses. The implications include shedding insights to public health officials on sentiments and opinions shaping public discourse on malaria epidemic. The multi-dimensional analysis of data provides directions for future research and informs public policy decisions

Multimedia Steganography, using RSA and Huffman code algorithms with LSB Insertion

The aim of this study is to develop an android application to transmit secret messages embedded on multimedia files (images, audio, and video)

Multi-path Sequential Fine-tuning of Pre-trained Models for Medical Image Classification Part I

https://scholar.dsu.edu/research-symposium/1018/thumbnail.jp

INCORPORATING SEQUENTIAL FEATURE MAPS FEEDING INTO MULTIPLE PATHS

https://scholar.dsu.edu/research-symposium/1007/thumbnail.jp

Malaria Surveillance System Using Social Media

Mixtures of von Mises-Fisher distributions have been shown to be an effective model for clustering data on a unit hypersphere, but variable selection for these models remains an important and challenging problem. In this paper, we derive two variants of the Expectation-Maximization (EM) framework, which are each used to identify a specific type of irrelevant clustering variable in these models. The first type are noise variables, which are not useful for separating any pairs of clusters. The second type are redundant variables, which may be useful for separating pairs of clusters, but do not enable any additional separation beyond the separability provided by some other variable. Removing these irrelevant variables is shown to improve cluster quality in simulated as well as benchmark text datasets

Application of Transfer Learning Techniques for Medical Image Classification

Recently, the healthcare industry is in a dynamic transformation accelerated by the availability of new artificial intelligence, machine learning, and deep learning (DL) technologies, tools and strategies facilitated by powerful graphical processing unit computing. The deployment of DL models in healthcare organizations for medical image analysis, is creating healthcare use cases, for example, increasing timeliness and accuracy of diagnosis thus improving healthcare outcomes and enhancing better medical decisions. Transfer learning (TL) techniques leverages DL algorithmic architecture to perform medical image analysis and classification which reduces physician’s workload, decreases time and costs for interpretation and thus helping physicians to focus more on improving patient care. In this research, we apply TL techniques in medical image classification tasks, namely feature extraction and fine-tuning strategies. We investigate the effectiveness of TL techniques on medical image classification using the Chest X-ray dataset. For our DL model, we used DenseNet-121 architecture, a deep convolutional neural network (DCNN) comprised of 121-layers, as the baseline model to perform a binary classification on the medical images. Applying fine-tuning strategy, freeze-unfreeze method of DCNN top layers and with data augmentation is an effective technique to mitigate overfitting and improve model performance. Using the fine-tuning strategy, we determined improved model performance at Conv5_block16_2_conv as the Optimal Cut-Off Layer. This research will extend its focus on additional fine-tuning approaches, such as hyperparameter optimization, finding optimal data augmentation and generating high resolution medical images using generative adversarial networks to determine the optimal behavior of an effective TL for medical image classification. Key words: Deep learning, medical image classification, transfer learning, healthcare, DenseNet-121 architecture, data augmentation