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    Wyświetlanie 1-5 z 5
    Tytuł:
    Breast cancer cad system by using transfer learning and enhanced ROI
    Autorzy:
    Al-Huseiny, Muayed S
    Sajit, Ahmed S
    Powiązania:
    https://bibliotekanauki.pl/articles/2097433.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    mammography
    transfer learning
    computer vision
    image processing
    Opis:
    Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.
    Źródło:
    Applied Computer Science; 2022, 18, 1; 99--111
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    ADDP : Anomaly Detection Based on Denoising Pretraining
    Autorzy:
    Ge, Xianlei
    Li, Xiaoyan
    Zhang, Zhipeng
    Powiązania:
    https://bibliotekanauki.pl/articles/27311945.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    Anomaly Detection
    Diffusion Models
    image denoising
    Pretraining
    transfer learning
    Opis:
    Acquiring labels in anomaly detection tasks is expensive and challenging. Therefore, as an effective way to improve efficiency, pretraining is widely used in anomaly detection models， which enriches the model's representation capabilities, thereby enhancing both performance and efficiency in anomaly detection. In most pretraining methods, the decoder is typically randomly initialized. Drawing inspiration from the diffusion model, this paper proposed to use denoising as a task to pretrain the decoder in anomaly detection, which is trained to reconstruct the original noise-free input. Denoising requires the model to learn the structure, patterns, and related features of the data, particularly when training samples are limited. This paper explored two approaches on anomaly detection: simultaneous denoising pretraining for encoder and decoder, denoising pretraining for only decoder. Experimental results demonstrate the effectiveness of this method on improving model’s performance. Particularly, when the number of samples is limited, the improvement is more pronounced.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 4; 719--726
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identifying selected diseases of leaves using deep learning and transfer learning models
    Autorzy:
    Mimi, Afsana
    Zohura, Sayeda Fatema Tuj
    Ibrahim, Muhammad
    Haque, Riddho Ridwanul
    Farrok, Omar
    Jabid, Taskeed
    Ali, Md Sawkat
    Powiązania:
    https://bibliotekanauki.pl/articles/2204260.pdf
    Data publikacji:
    2023
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
    Tematy:
    convolutional neural network
    transfer learning
    leaf disease detection
    image classification
    Opis:
    Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus (bright eyes) and scorched leaves in Fragaria ×ananassa (strawberry) plants. In this work, we explore approaches to use computer vision techniques to help plant owners identify such leaf disorders in their plants automatically and conveniently. This research designs three machine learning systems, namely a vanilla CNN model, a CNN-SVM hybrid model, and a MobileNetV2-based transfer learning model that detect yellowed and scorched leaves in Catharanthus roseus and strawberry plants, respectively, using images captured by mobile phones. In our experiments, the models yield a very promising accuracy on a dataset having around 4000 images. Of the three models, the transfer learning-based one demonstrates the highest accuracy (97.35% on test set) in our experiments. Furthermore, an Android application is developed that uses this model to allow end-users to conveniently monitor the condition of their plants in real time.
    Źródło:
    Machine Graphics & Vision; 2023, 32, 1; 55--71
    1230-0535
    2720-250X
    Pojawia się w:
    Machine Graphics & Vision
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Selected technical issues of deep neural networks for image classification purposes
    Autorzy:
    Grochowski, Michał
    Kwasigroch, A.
    Mikołajczyk, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/200871.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep neural network
    deep learning
    image classification
    batch normalization
    transfer learning
    dropout
    sieć neuronowa
    klasyfikacja obrazów
    normalizacja
    transfer nauki
    uczenie głębokie
    Opis:
    In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 2; 363-376
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Utilizing relevant RGB-D data to help recognize RGB images in the target domain
    Autorzy:
    Gao, Depeng
    Liu, Jiafeng
    Wu, Rui
    Cheng, Dansong
    Fan, Xiaopeng
    Tang, Xianglong
    Powiązania:
    https://bibliotekanauki.pl/articles/329725.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    object recognition
    RGB-D image
    transfer learning
    privileged information
    rozpoznawanie obiektu
    obraz RGB-D
    uczenie maszynowe
    informacja poufna
    Opis:
    With the advent of 3D cameras, getting depth information along with RGB images has been facilitated, which is helpful in various computer vision tasks. However, there are two challenges in using these RGB-D images to help recognize RGB images captured by conventional cameras: one is that the depth images are missing at the testing stage, the other is that the training and test data are drawn from different distributions as they are captured using different equipment. To jointly address the two challenges, we propose an asymmetrical transfer learning framework, wherein three classifiers are trained using the RGB and depth images in the source domain and RGB images in the target domain with a structural risk minimization criterion and regularization theory. A cross-modality co-regularizer is used to restrict the two-source classifier in a consistent manner to increase accuracy. Moreover, an L2,1 norm cross-domain co-regularizer is used to magnify significant visual features and inhibit insignificant ones in the weight vectors of the two RGB classifiers. Thus, using the cross-modality and cross-domain co-regularizer, the knowledge of RGB-D images in the source domain is transferred to the target domain to improve the target classifier. The results of the experiment show that the proposed method is one of the most effective ones.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 611-621
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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