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    Wyświetlanie 1-7 z 7
    Tytuł:
    A CNN Approach to Central Retinal Vein Occlusion Detection
    Autorzy:
    Bala, Jayanthi Rajee
    Sindha, Mohamed Mansoor Roomi
    Sahayam, Jency
    Govindharaj, Praveena
    Rakesh, Karthika Priya
    Powiązania:
    https://bibliotekanauki.pl/articles/27311911.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    Blood vessels
    segmentation
    Features
    CRVO
    deep learning
    Opis:
    In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 3; 565--570
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks
    Autorzy:
    Prajapati, Hardik K.
    Joshi, Rutvij
    Powiązania:
    https://bibliotekanauki.pl/articles/2200710.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    machine learning
    Deep learning
    Convolutional Neural Network (CNN)
    LEACH
    Opis:
    Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.We used a combination of LEACH and deep learning to extend the network life of the WSN in this study. In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 4; 799--805
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques
    Autorzy:
    Sherif, Fatma
    Mohamed, Wael A.
    Mohra, A.S.
    Powiązania:
    https://bibliotekanauki.pl/articles/226719.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    melanoma
    skin cancer
    convolutional neural network
    deep learning
    Opis:
    In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 4; 597-602
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process
    Autorzy:
    Goay, Chan Hong
    Cheong, Zheng Quan
    Low, Chen En
    Ahmad, Nur Syazreen
    Goh, Patrick
    Powiązania:
    https://bibliotekanauki.pl/articles/2200709.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    adaptive sampling
    deep neural networks
    deep learning
    power-ground plane
    Z-parameters
    Opis:
    This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the nonnormalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a squareshaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 4; 793--798
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Image Features in Music Information Retrieval
    Autorzy:
    Gwardys, G.
    Grzywczak, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/226400.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    music information retrieval
    deep learning
    genre classification
    convolutional neural networks
    transfer learning
    Opis:
    Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model trained on CNN were applied to a Music Information Retrieval (MIR), in particular to musical genre recognition. The model was trained on ILSVRC-2012 (more than 1 million natural images) to perform image classification and was reused to perform genre classification using spectrograms images. Harmonic/percussive separation was applied, because it is characteristic for musical genre. At final stage, the evaluation of various strategies of merging Support Vector Machines (SVMs) was performed on well known in MIR community - GTZAN dataset. Even though, the model was trained on natural images, the results achieved in this study were close to the state-of-the-art.
    Źródło:
    International Journal of Electronics and Telecommunications; 2014, 60, 4; 321-326
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques
    Autorzy:
    Eldeeb, Ahmed H.
    Amr, Mohammed Nagah
    Ibrahim, Amin S.
    Kamel, Hesham
    Fouad, Sara
    Powiązania:
    https://bibliotekanauki.pl/articles/2200729.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Deep learning
    COVID-19
    Artificial Intelligence
    computed tomography
    Convolutional Neural Networks
    Opis:
    The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 4; 731--739
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Learning Can Improve Early Skin Cancer Detection
    Autorzy:
    Mohamed, Abeer
    Mohamed, Wael A.
    Zekry, Abdel Halim
    Powiązania:
    https://bibliotekanauki.pl/articles/963798.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technology
    dermoscopic lesions
    convolutional
    neural network
    ISIC dataset
    deep learning
    neural networks
    Opis:
    Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 3; 507-512
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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