BackgroundMicroarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples. In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in gene selection. The popular regularization technique is Lasso (L1), and many L1 type regularization terms have been proposed in the recent years. Theoretically, the Lq type regularization with the lower value of q would lead to better solutions with more sparsity. Moreover, the L1/2 regularization can be taken as a representative of Lq (0
Cataract constitutes half of the blindness cases worldwide; hence, detecting and treating cataracts in a timely manner are effective strategies for blindness prevention. Recently, methods of detecting cataracts through deep learning are flourishing; however, the task of improving the grading mechanism is still the priority in the research field. This study evaluates the classification capability of the automated nuclear cataract detection algorithm using ocular images captured by smartphone-based slit-lamp. The task of the algorithm is to automatically detect cataract severity in terms of the photometric appearance of the nuclear region of the crystalline lens of the eyes. The nuclear region of the ocular lens was localized by YOLOv3. Subsequently, the combination of a deep learning network, ShuffleNet, and a support vector machine (SVM) classifier was used to grade cataract severity, evaluating the gray conjugate features of the nuclear region. Using the trained algorithm, 819 anterior ocular images captured by smartphone-based slit-lamp were utilized to evaluate the algorithm's performance. The accuracy was 93.5% with Kappa of 95.4% and F1 of 92.3%. The AUC was 0.9198. The proposed validation method could evaluate a cataract severity in 29 ms and the entire classification process in less than 1s. This study can improve the accuracy of the examination, reduce misdiagnosis rate and the difficulty of the doctor's examination. The addition of scoring system can improve the quality of pictures obtained by non-ophthalmologists. The method is especially suitable for cataract screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment. INDEX TERMS images captured by smartphone-based slit-lamp, automated cataract detection, grade cataract, deep learning.
Purpose. As part of plans to provide help to people in remote and poor areas who have no medical resources, a portable slit-lamp based on a smartphone was proposed. This would help in early screening of cataract diseases. Methods. This means a microlens is designed that would work with a phone’s camera. The phone’s photo taking function is used in capturing the image of the eyes lens to replace the observation system of the desktop slit-lamp. A simplified slit light band was designed. In order for the light source part to meet the portable requirements of the slit-lamp, the adjustable and diffused light functions of the ligaments were removed in this design. Furthermore, the images collected by the smartphone are uploaded to the deep learning cataract screening system, which can achieve real-time and effective screening of cataract. Results. Unlike the desktop slit-lamp, which needs skilled personnel to operate, this device can be easily operated by less-skilled or inexperienced doctors. This eliminates the concerns of inaccurate diagnosis based on the use of unskilled professionals. Due to the portability, ease of use, and simplicity in obtaining crystal images of this device, it serves as a promising platform for nonhospital screening and telemedicine. Conclusions. In this paper, we invented a small portable device for screening cataract. This device is to make screening and diagnosis of cataract in remote areas very fast and effective. It will also solve the problem of inadequate specialized doctors and equipment in those areas as well. Translational Relevance. Smartphones can be used with portable slit-lamps to capture the images of the lens.
Purpose A real-time automatic cataract-grading algorithm based on cataract video is proposed. Materials and methods In this retrospective study, we set the video of the eye lens section as the research target. A method is proposed to use YOLOv3 to assist in positioning, to automatically identify the position of the lens and classify the cataract after color space conversion. The data set is a cataract video file of 38 people's 76 eyes collected by a slit lamp. Data were collected using five random manner, the method aims to reduce the influence on the collection algorithm accuracy. The video length is within 10 s, and the classified picture data are extracted from the video file. A total of 1520 images are extracted from the image data set, and the data set is divided into training set, validation set and test set according to the ratio of 7:2:1. Results We verified it on the 76-segment clinical data test set and achieved the accuracy of 0.9400, with the AUC of 0.9880, and the F1 of 0.9388. In addition, because of the color space recognition method, the detection per frame can be completed within 29 microseconds and thus the detection efficiency has been improved significantly. Conclusion With the efficiency and effectiveness of this algorithm, the lens scan video is used as the research object, which improves the accuracy of the screening. It is closer to the actual cataract diagnosis and treatment process, and can effectively improve the cataract inspection ability of non-ophthalmologists. For cataract screening in poor areas, the accessibility of ophthalmology medical care is also increased.
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