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Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning.

Alhanoof Althnian¹, Nada Almanea¹, Nourah Aloboud²

¹Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This study introduces a smartphone-based approach for diagnosing neonatal jaundice. Deep transfer learning and traditional machine learning models were compared, with transfer learning showing promise for skin images.

Keywords:

CNN MLP SVM deep learning diagnosis healthcare jaundice machine learning smartphone sensor transfer learning

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Area of Science:

Medical Imaging
Computational Biology
Neonatal Medicine

Background:

Neonatal jaundice is a prevalent global condition.
Delayed diagnosis and treatment can result in severe outcomes like brain injury or death.
Current diagnostic methods involve invasive blood tests or expensive non-invasive devices.

Purpose of the Study:

To develop and evaluate non-invasive diagnostic tools for neonatal jaundice using smartphone imaging.
To compare the efficacy of deep transfer learning against traditional machine learning models.
To assess the performance of models using eye, skin, and fused image datasets.

Main Methods:

A dataset of neonatal images was collected using a smartphone camera.
Deep transfer learning models were applied to eye, skin, and fused images.
Traditional machine learning models (MLP, SVM, DT, RF) were trained and compared.
Performance metrics including accuracy, precision, recall, F-score, and AUC were statistically analyzed.

Main Results:

The deep transfer learning model achieved the best performance using skin images.
Traditional machine learning models excelled with eye and fused image features.
The transfer learning model with skin features demonstrated performance comparable to the MLP model using eye features.

Conclusions:

Smartphone-based imaging combined with machine learning offers a viable non-invasive approach for neonatal jaundice diagnosis.
Deep transfer learning and traditional models show distinct strengths depending on the image modality used.
Further development of these tools could improve accessibility and timeliness of diagnosis for neonatal jaundice.