Summary of Bmi Prediction From Handwritten English Characters Using a Convolutional Neural Network, by N. T. Diba et al.
BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network
by N. T. Diba, N. Akter, S. A. H. Chowdhury, J. E. Giti
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning approach uses a convolutional neural network (CNN) to estimate body mass index (BMI) from handwritten characters. The study develops a CNN-based model that predicts BMI from a dataset containing samples from 48 people in lowercase English scripts, achieving an accuracy of 99.92%. Compared to other popular CNN architectures, AlexNet and InceptionV3 achieve the second and third-best performance with accuracies of 99.69% and 99.53%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses deep learning techniques to predict BMI from handwritten characters. The study develops a model that estimates BMI by analyzing handwriting samples from 48 people. The results show that this approach is accurate, with an accuracy rate of 99.92%. This technology has the potential to be used in various applications, such as determining nutritional status. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Neural network
