BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

567 related articles for article (PubMed ID: 35214531)

  • 21. White blood cells detection and classification based on regional convolutional neural networks.
    Kutlu H; Avci E; Özyurt F
    Med Hypotheses; 2020 Feb; 135():109472. PubMed ID: 31760248
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features.
    Khalid A; Senan EM; Al-Wagih K; Al-Azzam MMA; Alkhraisha ZM
    Diagnostics (Basel); 2023 May; 13(9):. PubMed ID: 37175045
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images.
    Ferjaoui R; Cherni MA; Boujnah S; Kraiem NEH; Kraiem T
    Comput Methods Programs Biomed; 2021 Sep; 209():106320. PubMed ID: 34390938
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.
    Ahmed IA; Senan EM; Shatnawi HSA; Alkhraisha ZM; Al-Azzam MMA
    Diagnostics (Basel); 2023 Mar; 13(6):. PubMed ID: 36980334
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.
    Kilicarslan S; Adem K; Celik M
    Med Hypotheses; 2020 Apr; 137():109577. PubMed ID: 31991364
    [TBL] [Abstract][Full Text] [Related]  

  • 26. A robust classification of acute lymphocytic leukemia-based microscopic images with supervised Hilbert-Huang transform.
    Elrefaie RM; Mohamed MA; Marzouk EA; Ata MM
    Microsc Res Tech; 2024 Feb; 87(2):191-204. PubMed ID: 37715495
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images.
    Jha KK; Dutta HS
    Med Biol Eng Comput; 2020 Jan; 58(1):171-186. PubMed ID: 31811554
    [TBL] [Abstract][Full Text] [Related]  

  • 28. IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.
    Bibi N; Sikandar M; Ud Din I; Almogren A; Ali S
    J Healthc Eng; 2020; 2020():6648574. PubMed ID: 33343851
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.
    McAllister P; Zheng H; Bond R; Moorhead A
    Comput Biol Med; 2018 Apr; 95():217-233. PubMed ID: 29549733
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional Neural Networks (CNNs).
    Albeeshi AA; Alshanbari HS
    Curr Med Imaging; 2023; 19(7):734-748. PubMed ID: 36239727
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Deep Learning-Based Methods for Automatic Diagnosis of Skin Lesions.
    El-Khatib H; Popescu D; Ichim L
    Sensors (Basel); 2020 Mar; 20(6):. PubMed ID: 32245258
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas.
    Hamdi M; Senan EM; Jadhav ME; Olayah F; Awaji B; Alalayah KM
    Diagnostics (Basel); 2023 Jul; 13(13):. PubMed ID: 37443652
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method.
    Chen YM; Chou FI; Ho WH; Tsai JT
    BMC Bioinformatics; 2022 Jan; 22(Suppl 5):615. PubMed ID: 35016610
    [TBL] [Abstract][Full Text] [Related]  

  • 34. A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors.
    Chen B; Zhang L; Chen H; Liang K; Chen X
    Comput Methods Programs Biomed; 2021 Mar; 200():105797. PubMed ID: 33317871
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier.
    Mirmohammadi P; Ameri M; Shalbaf A
    Phys Eng Sci Med; 2021 Jun; 44(2):433-441. PubMed ID: 33751420
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A convolutional neural network-based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction.
    Anwar S; Alam A
    Med Biol Eng Comput; 2020 Dec; 58(12):3113-3121. PubMed ID: 33159270
    [TBL] [Abstract][Full Text] [Related]  

  • 37. An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.
    Yazdan SA; Ahmad R; Iqbal N; Rizwan A; Khan AN; Kim DH
    Tomography; 2022 Jul; 8(4):1905-1927. PubMed ID: 35894026
    [TBL] [Abstract][Full Text] [Related]  

  • 38. IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification.
    Pałczyński K; Śmigiel S; Gackowska M; Ledziński D; Bujnowski S; Lutowski Z
    Sensors (Basel); 2021 Dec; 21(23):. PubMed ID: 34884029
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.
    Nguyen DT; Pham TD; Baek NR; Park KR
    Sensors (Basel); 2018 Feb; 18(3):. PubMed ID: 29495417
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.
    Li F; Zhao C; Xia Z; Wang Y; Zhou X; Li GZ
    BMC Complement Altern Med; 2012 Aug; 12():127. PubMed ID: 22898352
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 29.