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- Title
- MULTI-MODEL DEEP LEARNING FOR GROUPER SOUND CLASSIFICATION AND SEIZURE PREDICTION.
- Creator
- Ibrahim, Ali K., Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Deep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and...
Show moreDeep learning models have been successfully applied to a variety of machine learning tasks, including image identification, image segmentation, object detection, speaker recognition, natural language processing, bioinformatics and drug discovery, among other things. This dissertation introduces Multi-Model Deep Learning (MMDL), a new ensemble deep learning approach for signal classification and event forecasting. The ultimate goal of the MMDL method is to improve classification and forecasting performances of individual classifiers by fusing results of participating deep learning models. The performance of such an ensemble model, however, depends heavily on the following two design features. Firstly, the diversity of the participating (or base) deep learning models is crucial. If all base deep learning models produce similar classification results, then combining these results will not provide much improvement. Thus, diversity is considered to be a key design feature of any successful MMDL system. Secondly, the selection of a fusion function, namely, a suitable function to integrate the results of all the base models, is important. In short, building an effective MMDL system is a complex and challenging process which requires deep knowledge of the problem context and a well-defined prediction process. The proposed MMDL method utilizes a bank of Convolutional Neural Networks (CNNs) and Stacked AutoEncoders (SAEs). To reduce the design complexity, a randomized generation process is applied to assign values to hyperparameters of base models. To speed up the training process, new feature extraction procedures which captures time-spatial characteristics of input signals are also explored. The effectiveness of the MMDL method is validated in this dissertation study with three real-world case studies. In the first case study, the MMDL model is applied to classify call types of groupers, an important fishery resource in the Caribbean that produces sounds associated with reproductive behaviors during yearly spawning aggregations. In the second case study, the MMDL model is applied to detect upcalls of North Atlantic Right Whales (NARWs), a type of endangered whales. NARWs use upcalls to communicate among themselves. In the third case study, the MMDL model is modified to predict seizure episodes. In all these cases, the proposed MMDL model outperforms existing state-of-the-art methods.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013382
- Subject Headings
- Deep Learning, Machine Learning, Neural networks (Computer science), Groupers, Whales, Vocalization, Animal, Seizures
- Format
- Document (PDF)
- Title
- MACHINE LEARNING DEMODULATOR ARCHITECTURES FOR POWER-LIMITED COMMUNICATIONS.
- Creator
- Gorday, Paul E., Nurgun, Erdol, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited...
Show moreThe success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited communication systems, such as those found in wireless sensor networks. Such networks will provide greater connection to the world around us and are expected to contain orders of magnitude more devices than cellular networks. A number of standard and proprietary protocols span this space, with modulations such as frequency-shift-keying (FSK), Gaussian FSK (GFSK), minimum shift keying (MSK), on-off-keying (OOK), and M-ary orthogonal modulation (M-orth). These modulations enable low-cost radio hardware with efficient nonlinear amplification in the transmitter and noncoherent demodulation in the receiver.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013511
- Subject Headings
- Deep learning, Machine learning--Technique, Demodulators, Wireless sensor networks, Computer network architectures
- Format
- Document (PDF)
- Title
- META-LEARNING AND ENSEMBLE METHODS FOR DEEP NEURAL NETWORKS.
- Creator
- Liu, Feng, Dingding, Wang, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Deep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep...
Show moreDeep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep learning equipped with meta learning and ensemble learning to study specific problems. We first propose a new deep learning based method for suggestion mining. The major challenges of suggestion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multi-model Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013481
- Subject Headings
- Neural networks (Computer science), Deep learning, Neural Networks in Applications, Machine learning--Technique
- Format
- Document (PDF)
- Title
- FINANCIAL TIME-SERIES ANALYSIS WITH DEEP NEURAL NETWORKS.
- Creator
- Rimal, Binod, Hahn, William Edward, Florida Atlantic University, Department of Mathematical Sciences, Charles E. Schmidt College of Science
- Abstract/Description
-
Financial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the...
Show moreFinancial time-series data are noisy, volatile, and nonlinear. The classic statistical linear models may not capture those underlying structures of the data. The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabilities of a machine opens the door to developing sophisticated deep learning models to capture the nonlinearity and hidden information in the data. Creating a robust model by unlocking the power of a deep neural network and using real-time data is essential in this tech era. This study constructs a new computational framework to uncover the information in the financial time-series data and better inform the related parties. It carries out the comparative analysis of the performance of the deep learning models on stock price prediction with a well-balanced set of factors from fundamental data, macroeconomic data, and technical indicators responsible for stock price movement. We further build a novel computational framework through a merger of recurrent neural networks and random compression for the time-series analysis. The performance of the model is tested on a benchmark anomaly time-series dataset. This new computational framework in a compressed paradigm leads to improved computational efficiency and data privacy. Finally, this study develops a custom trading simulator and an agent-based hybrid model by combining gradient and gradient-free optimization methods. In particular, we explore the use of simulated annealing with stochastic gradient descent. The model trains a population of agents to predict appropriate trading behaviors such as buy, hold, or sell by optimizing the portfolio returns. Experimental results on S&P 500 index show that the proposed model outperforms the baseline models.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014009
- Subject Headings
- Neural networks (Computer science), Deep learning (Machine learning), Time-series analysis, Stocks, Simulated annealing (Mathematics)
- Format
- Document (PDF)
- Title
- DEEP LEARNING REGRESSION MODELS FOR LIMITED BIOMEDICAL TIME-SERIES DATA.
- Creator
- Hssayeni, Murtadha D., Behnaz Ghoraani, Behnaz, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Time-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning...
Show moreTime-series data in biomedical applications are gaining an increased interest to detect and predict underlying diseases and estimate their severity, such as Parkinson’s disease (PD) and cardiovascular diseases. This interest is driven by advances in wearable sensors and deep learning models to a large extent. In the literature, less attention has been paid to regression models for continuous outcomes in these applications, especially when dealing with limited data. Training deep learning models on raw limited data results in overfitted models, which is the main technical challenge we address in this dissertation. An example of limited and\or imbalanced time-series data is PD’s motion signals that are needed for the continuous severity estimation of Parkinson’s disease (PD). The significance of this continuous estimation is providing a tool for longitudinal monitoring of daily motor and non-motor fluctuations and managing PD medications. The dissertation objective is to train generalizable deep learning models for biomedical regression problems when dealing with limited training time-series data. The goal is designing, developing, and validating an automatic assessment system based on wearable sensors that can measure the severity of PD complications in the home-living environment while patients with PD perform their activities of daily living (ADL). We first propose using a combination of domain-specific feature engineering, transfer learning, and an ensemble of multiple modalities. Second, we utilize generative adversarial networks (GAN) and propose a new formulation of conditional GAN (cGAN) as a generative model for regression to handle an imbalanced training dataset. Next, we propose a dual-channel auxiliary regressor GAN (AR-GAN) trained using Wasserstein-MSE-correlation loss. The proposed AR-GAN is used as a data augmentation method in regression problems.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00013992
- Subject Headings
- Deep learning (Machine learning), Regression analysis--Mathematical models, Biomedical engineering
- Format
- Document (PDF)
- Title
- DATA-DRIVEN IDENTIFICATION AND CONTROL OF TURBULENT STRUCTURES USING DEEP NEURAL NETWORKS.
- Creator
- Jagodinski, Eric, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
- Abstract/Description
-
Wall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a...
Show moreWall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a fundamental viewpoint. One unexplained phenomenon is the formation and impact of coherent structures like the ejections of slow near-wall fluid into faster moving ow which have been shown to correlate with increases in friction drag. This thesis focuses on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the nonlinear nature of this phenomenon. Deep Learning has provided new avenues of analyzing large amounts of data by applying techniques modeled after biological neurons. These techniques allow for the discovery of nonlinear relationships in massive, complex systems like the data found frequently in fluid dynamics simulation. Using a neural network architecture called Convolutional Neural Networks that specializes in uncovering spatial relationships, a network was trained to estimate the relative intensity of ejection structures within turbulent flow simulation without any a priori knowledge of the underlying flow dynamics. To explore the underlying physics that the trained network might reveal, an interpretation technique called Gradient-based Class Activation Mapping was modified to identify salient regions in the flow field which most influenced the trained network to make an accurate estimation of these organized structures. Using various statistical techniques, these salient regions were found to have a high correlation to ejection structures, and to high positive kinetic energy production, low negative production, and low energy dissipation regions within the flow. Additionally, these techniques present a general framework for identifying nonlinear causal structures in general three-dimensional data in any scientific domain where the underlying physics may be unknown.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014119
- Subject Headings
- Turbulent flow, Turbulence, Neural networks (Computer science), Deep learning (Machine learning)
- Format
- Document (PDF)
- Title
- STUDY AND ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR DETECTION OF DISTRACTED DRIVERS.
- Creator
- Qu, Fangming, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The rise of Advanced Driver-Assistance Systems (ADAS) and Autonomous Vehicles (AVs) emphasizes the urgent need to combat distracted driving. This study introduces a fresh approach for improved detection of distracted drivers, combining a pre-trained Convolutional Neural Network (CNN) with a Bidirectional Long Short- Term Memory (BiLSTM) network. Our analysis utilizes both spatial and temporal features to examine a broad array of driver distractions. We demonstrate the advantage of this CNN...
Show moreThe rise of Advanced Driver-Assistance Systems (ADAS) and Autonomous Vehicles (AVs) emphasizes the urgent need to combat distracted driving. This study introduces a fresh approach for improved detection of distracted drivers, combining a pre-trained Convolutional Neural Network (CNN) with a Bidirectional Long Short- Term Memory (BiLSTM) network. Our analysis utilizes both spatial and temporal features to examine a broad array of driver distractions. We demonstrate the advantage of this CNN-BiLSTM framework over conventional methods, achieving significant precision (up to 98.97%) on the combined ’Union Dataset,’ merging the Kaggle State Farm Dataset and AUC Distracted Driver Dataset (AUC-DDD). This research enhances safety in autonomous vehicles by providing a solid and flexible solution for everyday use. Our results mark considerable progress in accurately identifying driver distractions, pushing the boundaries of safety technology in AVs.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014418
- Subject Headings
- Deep learning (Machine learning), Distracted driving, Transportation--Safety measures, Automated vehicles--Safety measures
- Format
- Document (PDF)
- Title
- FROM DNA TO GRAVITATIONAL WAVES: APPLICATIONS OF STATISTICS AND MACHINE LEARNING.
- Creator
- Alemrajabi, Mahsa Firouzabad, Tichy, Wolfgang, Assis, Raquel, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
- Abstract/Description
-
In the current world of fast-paced data production, statistics and machine learning tools are essential for interpreting and utilizing the full potential of this data. This dissertation comprises three studies employing statistical analysis and Convolutional Neural Network models. First, the research investigates the genetic evolution of the SARS-CoV-2 RNA molecule, emphasizing the role of epistasis in the RNA virus’s ability to adapt and survive. Through statistical tests, this study...
Show moreIn the current world of fast-paced data production, statistics and machine learning tools are essential for interpreting and utilizing the full potential of this data. This dissertation comprises three studies employing statistical analysis and Convolutional Neural Network models. First, the research investigates the genetic evolution of the SARS-CoV-2 RNA molecule, emphasizing the role of epistasis in the RNA virus’s ability to adapt and survive. Through statistical tests, this study validates the significant impacts of genetic interactions and mutations on the virus’s structural changes over time, offering insights into its evolutionary dynamics. Secondly, the dissertation explores medical diagnosis by implementing Convolutional Neural Networks to differentiate between lung CT-scans of COVID-19 and non-COVID patients. This portion of the research demonstrates the capability of deep learning to enhance diagnostic processes, thereby reducing time and increasing accuracy in clinical settings. Lastly, we delve into gravitational wave detection, an area of astrophysics requiring precise data analysis to identify signals from cosmic events such as black hole mergers. Our goal is to utilize Convolutional Neural Network models in hopes of improving the sensitivity and accuracy of detecting these difficult to catch signals, pushing the boundaries of what we can observe in the universe. The findings of this dissertation underscore the utility of combining statistical methods and machine learning models to solve problems that are not only varied but also highly impactful in their respective fields.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014454
- Subject Headings
- Neural networks (Computer science), Gravitational waves, Deep learning (Machine learning), Diagnosis, Epistasis, Genetic
- Format
- Document (PDF)
- Title
- DEEP LEARNING BASED ANOMALY DETECTION IN SPACE SYSTEMS AND OPERATIONS.
- Creator
- Akbarian, Hamid, Mahgoub, Imadeldin, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
The relentless expansion of space exploration necessitates the development of robust and dependable anomaly detection systems (ADS) to safeguard the safety and efficacy of space missions. Conventional anomaly detection methods often falter in the face of the intricate and nuanced dynamics of space systems, resulting in a proliferation of false positives and/or false negatives. In this study, we explore into cutting-edge techniques in deep learning (DL) to tackle the challenges inherent in ADS...
Show moreThe relentless expansion of space exploration necessitates the development of robust and dependable anomaly detection systems (ADS) to safeguard the safety and efficacy of space missions. Conventional anomaly detection methods often falter in the face of the intricate and nuanced dynamics of space systems, resulting in a proliferation of false positives and/or false negatives. In this study, we explore into cutting-edge techniques in deep learning (DL) to tackle the challenges inherent in ADS. This research offers an in-depth examination of recent breakthroughs and hurdles in deep learning-driven anomaly detection tailored specifically for space systems and operations. A key advantage of deep learning-based anomaly detection lies in its adaptability to the diverse data encountered in space missions. For instance, Convolutional Neural Networks (CNNs) excel at capturing spatial dependencies in high-dimensional data, rendering them well-suited for tasks such as satellite imagery analysis. Conversely, Recurrent Neural Networks (RNNs), with their temporal modeling prowess, excel in identifying anomalies in time-series data generated by spacecraft sensors. Despite the potential of deep learning, several challenges persist in its application to anomaly detection in space systems. The scarcity of labeled data presents a formidable hurdle, as acquiring labeled anomalies during space operations is often prohibitively expensive and impractical. Additionally, the interpretability of deep learning models remains a concern, particularly in mission-critical scenarios where human operators need to comprehend the rationale behind anomaly predictions.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014390
- Subject Headings
- Anomaly detection (Computer security), Outer space—Exploration, Deep learning (Machine learning), Neural networks (Computer science), Convolutional neural networks
- Format
- Document (PDF)
- Title
- Deep Learning for Android Application Ransomware Detection.
- Creator
- Wongsupa, Panupong, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Smartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All...
Show moreSmartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All which are currently hidden in a large number of benign apps in official Android markets, such as Google PlayStore, and Amazon. Ransomware is a malware that once infected the victim’s device. It will encrypt files, unlock device system, and display a popup message which asks the victim to pay ransom in order to unlock their device or system which may include medical devices that connect through the internet. In this research, we propose to combine permission and API calls, then use Deep Learning techniques to detect ransomware apps from the Android market. Permissions setting and API calls are extracted from each app file by using a python library called AndroGuard. We are using Permissions and API call features to characterize each application, which can identify which application has potential to be ransomware or is benign. We implement our Android Ransomware Detection framework based on Keras, which uses MLP with back-propagation and a supervised algorithm. We used our method with experiments based on real-world applications with over 2000 benign applications and 1000 ransomware applications. The dataset came from ARGUS’s lab [1] which validated algorithm performance and selected the best architecture for the multi-layer perceptron (MLP) by trained our dataset with 6 various of MLP structures. Our experiments and validations show that the MLPs have over 3 hidden layers with medium sized of neurons achieved good results on both accuracy and AUC score of 98%. The worst score is approximately 45% to 60% and are from MLPs that have 2 hidden layers with large number of neurons.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013151
- Subject Headings
- Deep learning, Android (Electronic resource)--Security measures, Malware (Computer software)--Prevention
- Format
- Document (PDF)
- Title
- Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
- Creator
- Mohammadi Khoroushadi, Mohammad Sadegh, Leventouri, Theodora, Zhuang, Hanqi, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
- Abstract/Description
-
Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient...
Show moreReducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013115
- Subject Headings
- Tomography--Image quality, Wavelets (Mathematics), Deep learning, Tomography, X-Ray Computed
- Format
- Document (PDF)
- Title
- Skin lesion segmentation and classification using deep learning.
- Creator
- Burdick, John B., Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Melanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed or left undiagnosed. Advances in artificial intelligence, particularly deep learning, have enabled the design and implementation of intelligent solutions to skin lesion detection and classification from visible light images, which are capable of performing early and accurate diagnosis of melanoma and other types of skin diseases. This work presents solutions to the problems of skin lesion segmentation and...
Show moreMelanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed or left undiagnosed. Advances in artificial intelligence, particularly deep learning, have enabled the design and implementation of intelligent solutions to skin lesion detection and classification from visible light images, which are capable of performing early and accurate diagnosis of melanoma and other types of skin diseases. This work presents solutions to the problems of skin lesion segmentation and classification. The proposed classification approach leverages convolutional neural networks and transfer learning. Additionally, the impact of segmentation (i.e., isolating the lesion from the rest of the image) on the performance of the classifier is investigated, leading to the conclusion that there is an optimal region between “dermatologist segmented” and “not segmented” that produces best results, suggesting that the context around a lesion is helpful as the model is trained and built. Generative adversarial networks, in the context of extending limited datasets by creating synthetic samples of skin lesions, are also explored. The robustness and security of skin lesion classifiers using convolutional neural networks are examined and stress-tested by implementing adversarial examples.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013021
- Subject Headings
- Melanoma, Medical imaging, Deep learning, Skin diseases--Classification, Image segmentation
- Format
- Document (PDF)
