SAJID FAROOQ
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Artigo IPEN-doc 29364 Exploring enamel demineralization from SEM images using deep learning algorithms2022 - 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 29303 Superior Machine Learning Method for breast cancer cell lines identification2022 - FAROOQ, SAJID; CARAMEL-JUVINO, AMANDA; DEL-VALLE, MATHEUS; SANTOS, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.We propose an artificial intelligence platform based on machine learning (ML) algorithm using Neighborhood Component analysis and K-Nearest Neighbors for breast cancer cell lines recognition. Our model presents up to 97% accuracy for identification of breast cancer cell lines.Artigo IPEN-doc 29302 Identification of enamel demineralization using high performance convolutional neural network2022 - 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.