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    Wyświetlanie 1-2 z 2
    Tytuł:
    Fast multispectral deep fusion networks
    Autorzy:
    Osin, V.
    Cichocki, A.
    Burnaev, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/200648.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multispectral imaging
    data fusion
    deep learning
    convolutional network
    object detection
    image segmentation
    obrazowanie wielospektralne
    fuzja danych
    uczenie głębokie
    sieci splotowe
    wykrywanie obiektów
    segmentacja obrazu
    Opis:
    Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 875-889
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Brain tumor classification in MRI imagesusing genetic algorithm appended CNN
    Autorzy:
    Balamurugan, Thiyagu
    Gnanamanoharan, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/38703164.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    deep learning
    convolutional neural networks
    EfficientNetB3
    genetic algorithm
    brain tumor classification
    głęboka nauka
    splotowe sieci neuronowe
    algorytm genetyczny
    klasyfikacja nowotworów mózgu
    Opis:
    Brain tumors are fatal for majority of the patients, the different nature of the tumorcells requires the use of combined medical measures, and categorizing such tumors isa difficult task for radiologists. The diagnostic structures based on PCs have been offeredas an aid in diagnosing a brain tumor using magnetic resonance imaging (MRI). Generalfunctions are retrieved from the lowest layers of the neural network, and these lowestlayers are responsible for capturing low-level features and patterns in the raw input data,which can be particularly unique to the raw image. To validate this, the EfficientNetB3pre-trained model is utilized to classify three types of brain tumors: glioma, meningioma,and pituitary tumor. Initially, the characteristics of several EfficientNet modules are takenfrom the pre-trained EfficientNetB3 version to locate the brain tumor. Three types of braintumor datasets are used to assess each approach. Compared to the existing deep learningmodels, the concatenated functions of EfficientNetB3 and genetic algorithms give betteraccuracy. Tensor flow 2 and Nesterov-accelerated adaptive moment estimation (Nadam)are also employed to improve the model training process by making it quicker and better.The proposed technique using CNN attains an accuracy of 99.56%, a sensitivity of 98.9%,a specificity of 98.6%, an F-score of 98.9%, a precision of 98.9%, and a recall of 99.54%.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 305-321
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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