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Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network
摘要： Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called 'Deep Retina.' Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.
关键词： deep convolutional neural network,mobile app,fractional max-pooling,support vector machine,diabetic retinopathy,fundus images,teaching-learning-based optimization
更新于2025-09-23 15:23:52
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An Efficient Non-Invasive Method to Fabricate In-Fiber Microcavities Using a Continuous-Wave Laser
摘要： Natural ecosystems exhibit complex dynamics of interacting species. Man-made ecosystems exhibit similar dynamics and, in the case of mobile app stores, can be said to perform optimization as developers seek to maximize app downloads. This work aims to understand stability and instability within app store dynamics and how it affects ?tness. The investigation is carried out with AppEco, a model of the iOS App Store, which was extended for this paper and updated to model the store from 2008 to 2014. AppEco models apps containing features, developers who build the apps, users who download apps according to their preferences, and an app store that presents apps to the users. It also models developers who use commonly observed strategies to build their apps: innovator, milker, optimizer, copycat, and ?exible (the ability to choose any strategy). Results show that despite the success of the copycat strategy, there is a clear stable state for low proportion of copycats in developer populations, mirroring results in theoretical biology for producer–scrounger systems. The results also show that the best ?tness is achieved when the evolutionary optimizer (as producer) and copycat (as scrounger) strategies coexist together in stable proportions.
关键词： mobile app developers,Agent-based simulation,evolutionary ecosystem model,genetic algorithms,app stores,producer–scrounger systems,computational modeling
更新于2025-09-23 15:19:57
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Mobile Based in Situ Detection of Live/Dead and Antibiotic Resistant Bacteria by Silver Nanorods Array Sensor Fabricated by Glancing Angle Deposition
摘要： The rapid in-situ detection of viability of bacteria is essential for human health and environmental care. It has become one of the biggest needs in biological and medical sciences to prevent infections and diseases, which usually occur in hospitals and field clinics. Nowadays, antibiotic resistance (ABR) has been grown as one of the world’s acutest public health problems, which requires a quick and efficient solution. Here, we demonstrate an easy, sensitive, user-friendly, portable, cost effective and time saving approach for detection of live, dead and drug resistant bacteria. Most of the organisms are found to produce H2S gas by their metabolism system. The endogenous H2S evolution was targeted to differentiate between live and dead as well as ABR bacteria. The silver nanorods (AgNRs) arrays sensors were fabricated by glancing angle deposition technique. The colorimetric and water wettability (contact angle) features of as-synthesized AgNRs were found to be highly sensitive and selective for hydrogen sulfide (H2S) gas. E.coli, P. aeruginosa, B. subtilis and S. aureus were used as the model organisms for this study. A drastic visible change in color as well as wetting properties of AgNRs array was observed. To make it easy, a user friendly and field deployable android based mobile app ‘Colorimetric Detector’ was developed. This dual mode detection is facile, inexpensive and can be easily scaled-up in the field of disease diagnosis.
关键词： mobile app,colorimetric detection,silver nanorods,antibiotic resistance,bacteria detection,H2S gas
更新于2025-09-11 14:15:04