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  • 1
    Online Resource
    Online Resource
    Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 3 ( 2023-01-17), p. 346-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 3 ( 2023-01-17), p. 346-
    Abstract: Background: Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers. Methods: This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset. Results: The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively). Conclusions: A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13030346
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 2
    Online Resource
    Online Resource
    Controlled Synthesis of Triangular Silver Nanoplates by Gelatin–Chitosan Mixture and the Influence of Their Shape on Antibacterial Activity
    MDPI AG ; 2019
    In:  Processes Vol. 7, No. 12 ( 2019-11-21), p. 873-
    Details
    In: Processes, MDPI AG, Vol. 7, No. 12 ( 2019-11-21), p. 873-
    Abstract: Triangular silver nanoplates were prepared by using the seeding growth approach with the presence of citrate-stabilized silver seeds and a mixture of gelatin–chitosan as the protecting agent. By understanding the critical role of reaction components, the synthesis process was improved to prepare the triangular nanoplates with high yield and efficiency. Different morphologies of silver nanostructures, such as triangular nanoplates, hexagonal nanoprisms, or nanodisks, can be obtained by changing experimental parameters, including precursor AgNO3 volume, gelatin–chitosan concentration ratios, and the pH conditions. The edge lengths of triangular silver nanoplates were successfully controlled, primarily through the addition of silver nitrate under appropriate condition. As-prepared triangular silver nanoplates were characterized by transmission electron microscopy (TEM), dynamic light scattering (DLS), UV-Vis, Fourier transform infrared spectroscopy (FT-IR), and X-Ray diffraction (XRD). Silver nanoplates had an average edge length of 65–80 nm depending on experimental conditions and exhibited a surface plasma resonance absorbance peak at 340, 450, and 700 nm. The specific interactions of gelatin and chitosan with triangular AgNPs were demonstrated by FT-IR. Based on the characterization, the growth mechanism of triangular silver nanoplates was theoretically proposed regarding the twinned crystal of the initial nanoparticle seeds and the crystal face-blocking role of the gelatin–chitosan mixture. Moreover, the antibacterial activity of triangular silver nanoplates was considerably improved in comparison with that of spherical shape when tested against Gram-positive and Gram-negative bacteria species, with 6.0 ug/mL of triangular silver nanoplates as the MBC (Minimum bactericidal concentration) for Escherichia coli and Vibrio cholera, and 8.0 ug/mL as the MBC for Staphylococcus aureus and Pseudomonas aeruginosa. The MIC (Minimum inhibitory concentration) of triangular Ag nanoplates was 4.0 ug/mL for E. coli, V. cholera, S. aureus, and P. aeruginosa.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr7120873
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2720994-5
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