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35 ta ish

Ish: 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

    Maqola201661 iqtibos
    ABI

  2. ImageNet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

    Maqola201723 iqtibos
    ABI

  3. Improvement of Image Enhancement Technique for Mammography Images

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

    MaqolaAdvanced Scientific Research Methods202321 iqtibos
    ABI

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

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

    Bob202318 iqtibos
    ABI

  5. MobileNetV2: Inverted Residuals and Linear Bottlenecks

    Mark Sandler, Andrew Howard, Menglong Zhu +2

    Preprint201817 iqtibos
    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

    MaqolaBiometric Identification and Security202217 iqtibos
    ABI

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

    Shavkat Fazilov, O.R. Yusupov

    MaqolaBiometric Identification and Security202116 iqtibos
    ABI

  8. Rethinking the Inception Architecture for Computer Vision

    Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe +2

    Maqola201614 iqtibos
    ABI

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

    Shavkat Fazilov, O.R. Yusupov

    MaqolaBiometric Identification and Security201914 iqtibos
    ABI

  10. Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia +6

    Maqola201511 iqtibos
    ABI

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

    Christian Szegedy, Sergey Ioffe, Sergey Ioffe +2

    Maqola20178 iqtibos
    ABI

  12. A Survey on Transfer Learning

    Sinno Jialin Pan, Qiang Yang

    Maqola20095 iqtibos
    ABI

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

    Zahra Rezaei

    Sharh maqola20213 iqtibos
    ABI

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

    Nima Tajbakhsh, J. Shin, Suryakanth Gurudu +4

    Maqola20163 iqtibos
    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

    Maqola20173 iqtibos
    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

    Maqola20183 iqtibos
    ABI

  17. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY

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

    Maqola20072 iqtibos
    ABI

  18. INbreast

    Inês Moreira, Igor Amaral, Inês Domingues +3

    Maqola20112 iqtibos
    ABI

  19. The Mammographic Image Analysis Society digital mammogram database

    John Suckling, James Parker, Susan Astley +9

    Maqola19942 iqtibos
    ABI

  20. Deep learning in medical imaging and radiation therapy

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

    Sharh maqola20182 iqtibos
    ABI

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

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

    Maqola20202 iqtibos
    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

    Maqola20212 iqtibos
    ABI

  23. Sarlavhasiz

    Boshqa1 iqtibos
    ABI

  24. Sarlavhasiz

    Boshqa1 iqtibos
    ABI

  25. Sarlavhasiz

    Boshqa1 iqtibos
    ABI