The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions
Abstract
:1. Introduction
1.1. Light Sources and Optimization
1.2. Periodontal Disease
1.3. Spectral Imaging for Machine Learning
1.4. The Aim and Contribution of This Study
2. Materials and Methods
2.1. The Color Observation Model
2.2. Alternating Optimization: A Light-Source Spectrum Optimization for Machine Learning
2.3. The Optimization of the Light-Source Spectrum Using a Neural Network
2.4. Problem Setting for Oral Lesions Detection
2.4.1. The One-vs-Rest Classification
2.4.2. The Input and Output
2.4.3. Initial Lighting Weights
- A randomized vector in the range of ;
- Weights which construct a cumulative SPD using weighted sub-lights that approximates the D65;
- Uniform weights with 1: .
2.4.4. The Cost Function for Alternating Optimization
2.4.5. The Cost Function for the Neural Network-Based Optimization
2.4.6. The Grid Search and Trials
2.5. The Materials for Oral Lesions’ Detection Problems
2.5.1. Light Sources
- Measured SPDs of 24 real LEDs in the 400–830 nm band;
- Simulated SPDs, with their mean aligned at even intervals, in the 400–1000 nm band.
2.5.2. The RGB Camera
2.5.3. Datasets
3. Results and Discussion
3.1. The Performance Comparison among Methods
3.2. The Effect of NIR Information and Specific Initial Lighting Weights
- The camera sensitivity in the NIR region does not differ between the three RGB channels (Figure 8) and no information appears in the RGB channel;
- Upon extending the wavelength range to 1000 nm and evenly spreading them, the variety of the cumulative SPD is lost, as represented by the sub-light in the distinct wavelength band of 400–800 nm.
3.3. The Optimal SPD and Stability
3.4. The Distances between Classes in the Enhanced Feature Space
4. Limitations and Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | Previous | Proposals | |
---|---|---|---|
Alternating Optimization (Linear Support Vector Machine) | Neural Network (NN) | Convolutional Neural Network (CNN) | |
Input | pixel | image | |
Task | One-vs-rest, 2-class Classification | ||
Target | Class of pix | Class of the central pixel | |
Light source |
| ||
Cross Validation | five-Fold | ||
Initial lighting weights | Randomized vector in |
Parameters | NN-Based | CNN-Based |
---|---|---|
# of layers | ||
# of units | ||
Activation | {Rectified linear unit (ReLU), None} | |
Conv2d | - | Size = 3 × 3, stride = 1, -channels |
Output layer | Softmax | |
Dropout |
Sub-Light | Peek Wavelength [nm] | |
---|---|---|
Measured | Simulated | |
1 | 405 | 412 |
2 | 420 | 437 |
3 | 435 | 462 |
4 | 450 | 487 |
5 | 470 | 512 |
6 | 490 | 537 |
7 | 505 | 562 |
8 | 525 | 587 |
9 | 535 | 612 |
10 | 555 | 637 |
11 | 565 | 662 |
12 | 570 | 687 |
13 | 590 | 712 |
14 | 600 | 737 |
15 | 610 | 762 |
16 | 625 | 787 |
17 | 630 | 812 |
18 | 645 | 837 |
19 | 660 | 862 |
20 | 670 | 887 |
21 | 680 | 912 |
22 | 690 | 937 |
23 | 700 | 962 |
24 | 780 | 987 |
Class | Previous | Proposal | |
---|---|---|---|
Alternating Optimization, | NN, | ||
Enamel | 11,836 | 11,836 | 11,836 |
Attrition and Erosion | 2500 | 2500 | 2500 |
Calculus | 1608 | 1608 | 1608 |
Initial Caries | 792 | 792 | 792 |
Microfracture | 900 | 900 | 900 |
Root | 897 | 897 | 897 |
Class | Sample Size | |
---|---|---|
One | Rest | |
Enamel | 11,836 | 6697 |
Attrition and Erosion | 2500 | 16,033 |
Calculus | 1608 | 16,925 |
Initial Caries | 792 | 17,741 |
Microfracture | 900 | 17,633 |
Root | 897 | 17,636 |
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Ito, K.; Higashi, H.; Hietanen, A.; Fält, P.; Hine, K.; Hauta-Kasari, M.; Nakauchi, S. The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions. J. Imaging 2023, 9, 7. https://doi.org/10.3390/jimaging9010007
Ito K, Higashi H, Hietanen A, Fält P, Hine K, Hauta-Kasari M, Nakauchi S. The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions. Journal of Imaging. 2023; 9(1):7. https://doi.org/10.3390/jimaging9010007
Chicago/Turabian StyleIto, Kenichi, Hiroshi Higashi, Ari Hietanen, Pauli Fält, Kyoko Hine, Markku Hauta-Kasari, and Shigeki Nakauchi. 2023. "The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions" Journal of Imaging 9, no. 1: 7. https://doi.org/10.3390/jimaging9010007