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35 та иш

Иш: A framework utilizing deep learning techniques for the detection and classification of breast cancerous cells in mammographic images

  1. Deep Residual Learning for Image Recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren +1

    Мақола201661 иқтибос
    ABI

  2. ImageNet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

    Мақола201723 иқтибос
    ABI

  3. Improvement of Image Enhancement Technique for Mammography Images

    Shavkat Fazilov, Kh.S. Abdiyeva, O.R. Yusupov

    МақолаAdvanced Scientific Research Methods202321 иқтибос
    ABI

  4. Patch-based lesion detection using deep learning method on small mammography dataset

    Shavkat Fazilov, Kh.S. Abdieva, O.R. Yusupov

    Боб202318 иқтибос
    ABI

  5. MobileNetV2: Inverted Residuals and Linear Bottlenecks

    Mark Sandler, Andrew Howard, Menglong Zhu +2

    Препринт201817 иқтибос
    ABI

  6. An Algorithm for Evaluating the Parameters Characterizing the Quality of the Image Field of the Iris by the Principal component analysis method

    Shavkat Fazilov, O.R. Yusupov, Kh.S. Abdiyeva

    МақолаBiometric Identification and Security202217 иқтибос
    ABI

  7. Improvement of the Daugman Method for Nonreference Assessment of Image Quality in Iris Biometric Technology

    Shavkat Fazilov, O.R. Yusupov

    МақолаBiometric Identification and Security202116 иқтибос
    ABI

  8. Rethinking the Inception Architecture for Computer Vision

    Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe +2

    Мақола201614 иқтибос
    ABI

  9. Algorithm for Extraction of the Iris Region in an Eye Image

    Shavkat Fazilov, O.R. Yusupov

    МақолаBiometric Identification and Security201914 иқтибос
    ABI

  10. Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia +6

    Мақола201511 иқтибос
    ABI

  11. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

    Christian Szegedy, Sergey Ioffe, Sergey Ioffe +2

    Мақола20178 иқтибос
    ABI

  12. A Survey on Transfer Learning

    Sinno Jialin Pan, Qiang Yang

    Мақола20095 иқтибос
    ABI

  13. A review on image-based approaches for breast cancer detection, segmentation, and classification

    Zahra Rezaei

    Шарҳ мақола20213 иқтибос
    ABI

  14. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

    Nima Tajbakhsh, J. Shin, Suryakanth Gurudu +4

    Мақола20163 иқтибос
    ABI

  15. A curated mammography data set for use in computer-aided detection and diagnosis research

    Rebecca Sawyer Lee, Francisco Javier Giménez Fuentes‐Guerra, Assaf Hoogi +3

    Мақола20173 иқтибос
    ABI

  16. Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets

    Ravi K. Samala, Heang‐Ping Chan, Lubomir M. Hadjiiski +3

    Мақола20183 иқтибос
    ABI

  17. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY

    Michael D. Heath, Kevin W. Bowyer, D B Kopans +1

    Мақола20072 иқтибос
    ABI

  18. INbreast

    Inês Moreira, Igor Amaral, Inês Domingues +3

    Мақола20112 иқтибос
    ABI

  19. The Mammographic Image Analysis Society digital mammogram database

    John Suckling, James Parker, Susan Astley +9

    Мақола19942 иқтибос
    ABI

  20. Deep learning in medical imaging and radiation therapy

    Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski +5

    Шарҳ мақола20182 иқтибос
    ABI

  21. Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study

    Laith Alzubaidi, Mohammed A. Fadhel, Omran Al-Shamma +4

    Мақола20202 иқтибос
    ABI

  22. A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

    Abeer Saber, Mohamed Sakr, Osama M. Abo-Seida +2

    Мақола20212 иқтибос
    ABI

  23. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI

  24. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI

  25. Сарлавҳасиз

    Бошқа1 иқтибос
    ABI