# A Deep Learning Technique for Optical Inspection of Color Contact Lenses
## 4 Results and Discussion
The optical transmittance of the fabricated hydrogels was measured at 300-700 nm and expressed as transmittance (%). The fabricated contact lenses exhibited high transmittance values (>94%), exceeding the optical transmittance required for wearable contact lenses (92%), as shown in Figure 13a. No opaque part could be observed on the lens surfaces with naked eyes, which is consistent with the spectroscopic results. These results imply that the networked p(HEMA) chains are uniformly distributed in the lens, causing no visible light scattering. The diameters of the prepared contact lenses were measured to be approximately 14.6 mm, corresponding to an overall diameter of 14.0-14.8 mm for most traditional soft contact lenses. The WC of the prepared contact lenses was measured to be approximately 38.4% using a drying oven. A WC level of approximately 38% feels comfortable and allows the eyes to breathe without becoming dried out, which renders it suitable for commercialized products. Following the completion of the above process, the contact lens defect was assessed using the CNN. Figure 12: Synthesis of hydrogel contact lenses via copolymerization of HEMA and EGDMA. Figure 13: (**a**) Transmittance spectra and (**b**) diameter observation of the prepared colored contact lenses. Figure 14 shows the trained GoogLeNet results of the contact lens defect detection for each of the six test images. The discrimination accuracies for each category were 83.3% for good products and 83.3, 16.7, 50, 83.3, 33.3, and 66.7% for bubble, crack, dust, foreign, print, and unformed defects, respectively. The low recognition rate can be attributed to the exceedingly small amount of data used, which appears to be sufficiently overcome if the amount of data is accumulated and increased in the future. In contrast to the defective data, the good data did not exhibit any singularities, and the recognition rate was greater than 83% for all backlight, ring-light, and spotlight images. A misrecognition rate of approximately 17% corresponded to assessing an unformed image that could not be identified by the naked eye as a good product. For bubbles, the recognition rate tended to decrease in the backlight image. As shown in the first image in Figure 14b, if bubbles were generated inside the area where the iris image was printed, it was determined as a bubble; however, as shown in the third image in Figure 14b, the inside of the iris image or the edge of the lens was classified as another category in the case of bubbles. In contrast, the ring and spotlight images exhibited an almost accurate recognition rate, as the characteristics of the bubbles appeared clearly. In the case of crack defects with the minimum amount of data, the absolute amount of learning data was insufficient. Consequently, the recognition rate was assessed as poor because it exhibited characteristics similar to those of unformed and foreign defects. Dust exhibited features similar to those of foreign matter in the backlight images, and clear dust features appeared in the ring and spotlight images. In the case of foreign defects, most of the images were assessed as foreign; however, certain images were recognized as unformed patterns. Further, in the case of print defects, the distinction was challenging owing to the various patterns of the iris. In addition, as shown in Figure 14f, it could be approximately distinguished in the backlight image; however, as the iris image was not visible in the ring- and spotlight images, the extraction of the feature points was difficult. This appeared to lower the recognition rate. In the case of unformed defects, the feature was clearly visible at the edge of the lens. However, in certain images, a tendency to confuse it with foreign defects was observed. This is shown in Figure 14g. Figure 14: _Cont._  
Figure 14: Test results by category: (**a**) good products, (**b**) bubble defect, (**c**) crack defect, (**d**) dust defect, (**e**) foreign defect, (**f**) print defect, (**g**) unformed defect.