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- W4377081331 abstract "Computer-Aided Diagnostic (CAD) techniques use image-based categorization as an effective method. Existing approaches rely primarily on structure, coloring, texture-based, and mixtures. The majority of these is issue-specific and demonstrate complementary in medical images, resulting in a scheme that cannot represent high-level problem functional requirements and has impoverished prototype generalization capability. Modern deep neural networks have made it possible to build an edge model which can generate the best classification categories from primary healthcare image data. The Disease Diagnostic Model utilizing Fine-Tuned Intensive Learning (DDM-FTIL) classification offers superior characteristics generated from a convolution neural network (CNN) with some classical characteristics. Adaptive mean filtering and adaptive histogram are used to strengthen and optimize the pixel density. We use the Gabor filter to convert the improved images. The characteristics are retrieved by the Gray-Level Co-occurrence Matrix (GLCM) as well as Local Binary Pattern (LBP) descriptors before classing the DDM-FTIL classifier. Oral cancer areas were segmented using morphometric approaches." @default.
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- W4377081331 date "2023-01-01" @default.
- W4377081331 modified "2023-10-18" @default.
- W4377081331 title "Disease Diagnostic Model Using Fine-Tuned Intensive Learning for Medical Image" @default.
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- W4377081331 doi "https://doi.org/10.1007/978-981-19-9090-8_8" @default.
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