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February 24, 2021

Deep Learning Healthcare Applications - Research Paper reads

Paper #1 - System for Recommending Facial Skincare Products

Key Notes

  • Multi-feature processing and classification of skin quality and acne status

Approach

  • K-means cluster to search for acne
  • Binary threshold using an adaptive method
  • Identified the location of acne and provided recommendations to consumers
  • A scale of oiliness was produced by labeling images of the weighted average

Steps

  • The first feature used to determine whether the skin is oily
  • Acne detection
  • The brightness image is subtracted from the normalized grayscale image

Paper #2 - A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images

Keynotes

  • Acne is the 8th most common skin disorder in the world
  • Transfer learning approach by extracting image features using a ResNet 152 pre-trained model, then adding and training a fully connected layer to learn the target severity level from labeled images.
  • Mobile application for acne assessment
  • Extracted skin patches from facial skins
  • Haar feature-based cascade classifier
  • Eye location, we inferred the regions of the forehead, cheeks and chin skin patches

Paper #3 - Deep Learning Methods for Selecting Appropriate Cosmetic Products for Various Skin Types: A Survey

Key Notes

  • The cosmetic data from various websites @cosme and @Nykaa gathered for this model evaluation.
  • The cosmetic product composition will be given based on skin types; dry, natural or oily.

Paper #4 Deep Learning Algorithms for Recognition of Facial Ageing Features

Keynotes

  • Wrinkles, Dark spots, Under-eye circles
  • Face Detection, wrinkle detection, scoring
  • Facial zone - ensemble of regression trees, retrained for 50 fiducial points (dlib implementation) + contours detection
  • Alignment - affine transformation
  • Wrinkles area detection - cut areas by support points

More Reads


Key Notes
  • Spectral Residual (SR) - Approach based on Fast Fourier Transform (FFT). Key Steps are
  • (1) Fourier Transform to get the log amplitude spectrum; 
  • (2) calculation of spectral residual; and 
  • (3) Inverse Fourier Transform that transforms the sequence back to the spatial domain

Visual saliency detection domain. Applying CNN on the basis of SR output directly
CNN as our discriminative model architecture

More Reads

Happy Learning!!!

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