SAJID FAROOQ

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Agora exibindo 1 - 2 de 2
  • Artigo IPEN-doc 29364
    Exploring enamel demineralization from SEM images using deep learning algorithms
    2022 - FAROOQ, SAJID; CARAMEL-JUVINO, AMANDA; FONTES, YASMIN R.; GARDIANO, SABRINA A.; ZEZELL, DENISE M.
    Here, we employ segmentation and convolutional neural network (CNN) to identify and quantify enamel demineralization. Our results depict that CNN model using input SEM images achieve accuracy up to 79% for enamel demineralization diagnosis.
  • Artigo IPEN-doc 29302
    Identification of enamel demineralization using high performance convolutional neural network
    2022 - CARAMEL-JUVINO, AMANDA; FAROOQ, SAJID; ROMANO, MARIANA; ZEZELL, DENISE M.
    Here, we traces use segmentation and convolutional neural network (CNN) to trace, diagnose and quantify enamel demineralization for research. The preprocessing, histograms based methods are used to enhance the contrast and equalize the brightness through the scanning electron microscope images. Our result evidence that the deep learning based CNN model is highly efficient to process the dental image to achieve high accuracy of enamel demineralization and presents promising outcomes with optimal precision.