Review Of Deep Learning Techniques Research Articles

Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based on Radiological Imaging

This literature review summarizes the current deep learning methods developed by the medical imaging AI research community that have been focused on resolving lung imaging problems related to coronavirus disease 2019 (COVID-19). COVID-19 shares many of the same imaging characteristics as other common forms of bacterial and viral pneumonia. Differentiating COVID-19 from other common pulmonary infections is a non-trivial task. To help offset what commonly requires hours of tedious manual annotation, several innovative solutions have been published to help healthcare providers during the COVID-19 pandemic. However, the absence of a comprehensive survey on the subject makes it challenging to ascertain which approaches are promising and therefore deserve further investigation. In this survey, we present an in-depth review of deep learning techniques that have recently been applied to the task of discovering the diagnosis and prognosis of COVID-19 patients. We categorize existing approaches based on features such as dimensionality of radiological imaging, system purpose, and used deep learning techniques, underlying core issues, and challenges. We also address the merits and shortcomings of various approaches, and finally we discuss future directions for this research.

Comprehensive Review of Deep learning Techniques in Electronic Medical Records

A digital collection of patient’s health care data like diagnosis history of patient, treatment details, medical prescriptions are stored electronically. This electronic patient health records (EPHR) model provides huge volume of real time data and used for clinical research. Natural Language processing (NLP) automatically retrieve the patient’s information based on decision support system. NLP performs traditional techniques of machine learning, deep learning algorithms and focussing on word embeddings, classification and prediction, extraction, knowledge graphs, phenotyping, etc. By using NLP technique, extract the information from clinical data and analysis it provides valuable patient medical information. NLP based on clinical systems are evaluated on document level annotations which contains document of patient report, health status of patient, document section types contain past medical history of patient, summary of discharge statement, etc. similarly the semantic properties contain severity of disease in the aspects of positivity, negativity. These documents are developed and implemented on word level or sentence level. In this survey article, we summarize the recent NLP techniques which are used in EPHR applications. This survey paper focuses on prediction, classification, extraction, embedding, phenotyping, multilingually etc techniques.

A Review of Deep Learning Techniques on Fundus Images for Detecting Diabetic Retinopathy on Public Datasets

The primary source of vision loss in patients is mainly due to Diabetic retinopathy (DR), caused due to diabetes mellitus. It has become a significant reason for visual impairment among people within 25-74 years of age. If timely medical attention is provided to DR patients, over 90% of people can be saved from vision loss. It's crucial for the early diagnosis of the disease and provide the necessary treatment. The symptoms are more prevalent in type 2 diabetics than associated with type 1 diabetics. Unlike computer-aided diagnosis systems, the traditional procedures of DR detection using fundus photography are both time and cost-consuming. Among the numerous methods for screening and detecting DR, Convolutional Neural Networks are considered extensively in Deep Learning (DL) methods. This review article illustrates the different datasets, pre-processing steps, and DL techniques used in the fundus images for efficient DR detection at an early stage. The main motive of this review article is to provide the research community with an insight into the various pre-processing steps, Public datasets, DL models in DR detection, and some future research directions in this field.

A Review of Deep Learning Techniques for Encrypted Traffic Classification

Network traffic classification is significant for task such as Quality of Services (QoS) provisioning, resource usage planning, pricing as well as in the context of security such as in Intrusion detection systems. The field has received considerable attention in the industry as well as research communities where approaches such as Port based, Deep packet Inspection (DPI), and Classical machine learning techniques were thoroughly studied. However, the emergence of new applications and encryption protocols as a result of continuous transformation of Internet has led to the rise of new challenges. Recently, researchers have employed deep learning techniques in the domain of network traffic classification in order to leverage the inherent advantages offered by deep learning models such as the ability to capture complex pattern as well as automatic feature learning. This paper reviews deep learning based encrypted traffic classification techniques, as well as highlights the current research gap in the literature. Index Terms : Traffic classification, Encrypted traffic, Deep learning, Machine learning.

Techniques of deep learning for diagnosing brain diseases: A review

Deep learning has been proved as a tremendous evolution in machine learning and computer vision. Through deep learning, computer machines become capable of solving the problems those were beyond the imagination two decades prior. Nowadays, Deep learning is significantly used in Natural language processing, image classification, medical science, handwriting recognition, face recognition, speech recognition, biometrics matching and in various real life problem domains. In this present paper, a review of deep learning techniques is presented for the diagnoses of brain diseases. Today, Deep learning is playing a crucial role in automating the medical equipment for the diagnosis of various brain diseases like tumor, Alzheimer, Mild Cognitive Impairment, brain hemorrhage, Parkinson etc. Deep learning has been tremendously used in detecting the severity of such diseases. This paper covers the recent approaches, techniques, learning algorithms of deep learning those have been used to detect major or minor diseases in a human brain. The paper also explores the future possibilities for Deep learning in medical science specifically for the brain diseases.

A Review of Deep Learning Techniques for Crowd Behavior Analysis

A Review of Deep Learning Techniques for Crowd Behavior Analysis

A Comprehensive Review of Deep Learning Techniques for the Detection of (Distributed) Denial of Service Attacks

(Distributed) Denial of Service (DoS/DDoS) attacks are performed to bring down a target by flooding it withuseless traffic. Because the DoS/DDoS attackers often change their styles and attack patterns, the nature andcharacteristics of these attacks need to be examined cautiously. Developing mechanisms to detect this menaceis a challenging task. Recently, deep learning has played a major role in the growth of intrusion detection solutions. In recent years, significant attempts have been made to construct deep learning models for counteringDoS/DDoS threats. In this review, we provide a taxonomy of DoS/DDoS attacks and deep learning-based DoS/DDoS detection approaches. Then, the article focuses on the recent (from 2016 onwards) defensive methodsagainst DoS/DDoS attacks that exploit the advantages of deep learning techniques and discusses the key features of each of them. As datasets are imperative for deep learning techniques, we also review the traditional and contemporary datasets that contain traces of DoS/DDoS attacks. The findings from the review articles are as well summarized and they urge that more effort be made to strengthen the existing state-of-the-art approaches to coping with the dynamic behavior of the attackers. The imbalances in the surveyed articles are also highlighted. Finally, we outline a few key research directions that will need additional focus in the near future to ensure good security against DoS/DDoS attacks using deep learning approaches.

Review of Deep Learning Techniques for Deepfake Image Detection

Review of Deep Learning Techniques for Deepfake Image Detection

Traffic flow prediction models – A review of deep learning techniques

Traffic flow prediction models – A review of deep learning techniques

Image Colorization Progress: A Review of Deep Learning Techniques for Automation of Colorization

Image colorization is the process of taking an input gray- scale (black and white) image and then producing an output colorized image that represents the semantic color tones of the input. Since the past few years, the process of automatic image colorization has been of significant interest and a lot of progress has been made in the field by various researchers. Image colorization finds its application in many domains including medical imaging, restoration of historical documents, etc. There have been different approaches to solve this problem using Convolutional Neural Networks as well as Generative Adversarial Networks. These colorization networks are not only based on different architectures but also are tested on varied data sets. This paper aims to cover some of these proposed approaches through different techniques. The results between the generative models and traditional deep neural networks are compared along with presenting the current limitations in those. The paper proposes a summarized view of past and current advances in the field of image colorization contributed by different authors and researchers.

Skin Cancer Detection: A Review Using Deep Learning Techniques.

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

A Review of Deep Learning Techniques in Document Image Word Spotting

A Review of Deep Learning Techniques in Document Image Word Spotting

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

Systematic review of deep learning techniques in plant disease detection

Automatic identification of diseases through hyperspectral images is a very critical and primary challenge for sustainable farming and gained the attention of researchers during the past few years. The technologies proposed, and techniques adopted so far are slighted in their scope and utterly contingent on deep learning models. The performance of convolutional neural networks is emerging as the most powerful tool to diagnose and predict the infections from the crop images. The present article has reviewed some of the existing neural network's techniques that are used to process image data with prominence on detecting crop diseases. First, a review of data acquisition sources, deep learning models/architectures, and different image processing techniques used to process the imaging data provided. Second, the study highlighted the results acquired from the evaluation of various existing deep learning models and finally mentioned the future scope for hyperspectral data analysis. The preparation of this survey is to allow future research to learn larger capabilities of deep learning while detecting plant diseases by improving system performance and accuracy.

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.