PMID- 30440941
OWN - NLM
STAT- MEDLINE
DCOM- 20191029
LR  - 20200928
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2018
DP  - 2018 Jul
TI  - A Two-Level Food Classification System For People With Diabetes Mellitus Using 
      Convolutional Neural Networks.
PG  - 2603-2606
LID - 10.1109/EMBC.2018.8512839 [doi]
AB  - Accurate estimation of food's macronutrient content for people with Diabetes 
      Mellitus (DM) is of great importance, as it determines postprandial insulin 
      dosage. This paper introduces a classification system for food images that is 
      adjusted to the nutritional needs of people with DM. A two-level image 
      classification scheme, exploiting Convolutional Neural Networks (CNNs), is 
      proposed, in order to classify an image in one of eight broad food categories 
      with similar macronutrient content and then assign it to a specific food within 
      that category. To this end, a visual dataset, namely NTUA-Food 2017, has been 
      designed, consisting of 3248 images organized in eight broad food categories of 
      totally 82 different foods. Moreover, a novel evaluation metric is proposed, 
      which penalizes classification errors proportionally to the discrepancy in 
      postprandial blood sugar levels between the actual and predicted class. The 
      proposed system achieves 84.18% and 85.94% classification accuracy at the first 
      and second level of classification, respectively, on the NTUA-Food 2017 dataset. 
      The algorithm developed for the first level of classification on the NTUA-Food 
      2017 dataset improves classification accuracy on the benchmark Food Image Dataset 
      (FID) to 97.08% outperforming previous approaches. The algorithm's mean error in 
      terms of carbohydrate content estimation on the NTUA-Food 2017 dataset is less 
      than 2 g per food serving.
FAU - Kogias, K
AU  - Kogias K
FAU - Andreadis, I
AU  - Andreadis I
FAU - Dalakleidi, K
AU  - Dalakleidi K
FAU - Nikita, K S
AU  - Nikita KS
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Algorithms
MH  - *Diabetes Mellitus
MH  - Food
MH  - Humans
MH  - Image Processing, Computer-Assisted
MH  - Neural Networks, Computer
EDAT- 2018/11/18 06:00
MHDA- 2019/10/30 06:00
CRDT- 2018/11/17 06:00
PHST- 2018/11/17 06:00 [entrez]
PHST- 2018/11/18 06:00 [pubmed]
PHST- 2019/10/30 06:00 [medline]
AID - 10.1109/EMBC.2018.8512839 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2603-2606. doi: 
      10.1109/EMBC.2018.8512839.