Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method

Abdullah, Hadeel N.; Abduljaleel, Hala K.

doi:10.30684/etj.37.11a.3

Cited by 18 publications

(11 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN models are essentially feed-forward artificial neural networks (ANNs). Basically, CNNs are a group of neural networks with multiple convolutional layers that may be trained so as to analyze images and extract features for the computer vision [27,28]. Designed to improve the use of spatial information through taking 2-D or 3-D images as input.…”

Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

View full text Add to dashboard Cite

The outbreak of the new coronavirus (COVID-19) has created a disaster worldwide and it became a very severe and acute disease. COVID-19 prevalence is rapidly increasing around the world. Deep learning (DL) technology had become a hot topic in the context of computing and is widely applied in various medical applications. These techniques have proven to be one of the effective tools for clinicians in the automatic diagnoses of COVID-19. The goal of the present paper is to provide an overview of recently developed systems based on DL techniques that use various medical imaging modalities such as Computer Tomography (CT) and Chest X-Rays (CXR). This review focuses on systems that had been developed for the diagnosis of COVID-19 with the use of the DL methods, as well as the well-known datasets that are utilized for the training of those networks. Finally, the researcher reviewed 58 research papers based on different medical images. Overall, this article aims to assist experts (medical or otherwise) and technicians to understand how the DL approaches are utilized in this context and the way that they can potentially be expanded to combat COVID-19 outbreaks.

show abstract

Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

View full text Add to dashboard Cite

show abstract

“…It found pervasiveness for handling issues connected with NLP assignments such as sentence classification, script classification, text summarization, conclusion analysis, machine translation and response relations. CNN is made of two real parts: Feature Extraction and Classification [21]- [23].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Convolutional Recurrent Neural Networks for Text Lecture Summarization

2022

IJCCCE

View full text Add to dashboard Cite

Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy. Index Terms— Text Summarization, Deep Learning, Convolutional Neural Network, Long Short Term Memory, Convolutional Recurrent Neural Network.

show abstract

“…The pooling techniques will be employed to reduce the size of the input as well as the convolution results. The output layer, on the other hand, will be a network of fully connected layers that will serve as a classifier for the retrieved features [37]- [39].…”

Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

View full text Add to dashboard Cite

<p><span lang="EN-US">Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.</span></p>

show abstract

Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method

Cited by 18 publications

References 7 publications

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Convolutional Recurrent Neural Networks for Text Lecture Summarization

Multimodal video abstraction into a static document using deep learning

Contact Info

Product

Resources

About