An Image Diagnosis Algorithm for Keratitis Based on Deep Learning

Ji, Qingbo; Jiang, Yue; Qu, Lijun; Yang, Qian; Zhang, Han

doi:10.1007/s11063-021-10716-2

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The article [53] has been published by the owners of the dataset used in this study. To compare the results of the proposed method with the results of other methods in which used the same dataset in the literature, the total 40 papers cited to the article [53] were initially retrieved from Web of Science (11), PubMed (7) and Google Scholar (22) databases. 25 of them were ignored because of duplicate papers.…”

Section: Resultsmentioning

confidence: 99%

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Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

Inneci,

Badem

2023

Bioengineering

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Corneal ulcer is one of the most devastating eye diseases causing permanent damage. There exist limited soft techniques available for detecting this disease. In recent years, deep neural networks (DNN) have significantly solved numerous classification problems. However, many samples are needed to obtain reasonable classification performance using a DNN with a huge amount of layers and weights. Since collecting a data set with a large number of samples is usually a difficult and time-consuming process, very large-scale pre-trained DNNs, such as the AlexNet, the ResNet and the DenseNet, can be adapted to classify a dataset with a small number of samples, through the utility of transfer learning techniques. Although such pre-trained DNNs produce successful results in some cases, their classification performances can be low due to many parameters, weights and the emergence of redundancy features that repeat themselves in many layers in som cases. The proposed technique removes these unnecessary features by systematically selecting images in the layers using a genetic algorithm (GA). The proposed method has been tested on ResNet on a small-scale dataset which classifies corneal ulcers. According to the results, the proposed method significantly increased the classification performance compared to the classical approaches.

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Section: Resultsmentioning

confidence: 99%

“…However, the detection of corneal ulcers requires high-quality facilities and ophthalmologists, which are not available in developing countries. Therefore, efficient alternative machine learning techniques can be used to support the ophthalmologist to diagnose corneal ulcers [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

Inneci,

Badem

2023

Bioengineering

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“…Akurasi yang terbaik dari studi ini yaitu di rentang 68-72%. Selanjutnya Ji et al (2022) melakukan studi dengan menggunakan metode ResNet50 untuk menghasilkan klasifikasi terhadap citra keratitis yang bersumber dari slitlamp. Hasil percobaan menunjukkan bahwa akurasi rata-rata mencapai 84.89% dalam jaringan multiatribut.…”

Section: Pendahuluanunclassified

Implementasi Pendekatan Algoritma Deep Learning CNN untuk Identifikasi Citra Pasien Keratitis

Agmalaro,

Kusuma,

Rif’ati

et al. 2023

JIKA

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Insiden keratitis secara global berkisar antara 0.4 sampai 5.2 per 10,000 orang setiap tahunnya. Penanggulangan gangguan penglihatan akibat keratitis secara dini dan akurat dapat mencegah kebutaan akibat kekeruhan kornea. Keratitis dapat diidentifikasi oleh dokter spesialis mata dengan bantuan slitlamp sebagai instrumen dasar pemeriksaan spesialistik organ mata di fasilitas layanan sekunder. Pada fasilitas layanan primer tidak tersedia dokter spesialis mata dan juga slitlamp. Hal ini meyebabkan terjadinya keterlambatan diagnosis dan penanganan terhadap pasien-pasien keratitis di puskesmas atau daerah-daerah dengan fasilitas serta akses ke dokter spesialis mata yang terbatas. Penelitian ini bertujuan untuk mengembangkan model identifikasi keratitis menggunakan metode convolutional neural network (CNN) dan data latih berupa citra yang dihasilkan oleh gawai pintar dan dikombinasikan dengan citra slitlamp. Akurasi pelatihan dari model yang dikembangkan adalah 92% dengan layer dropout ditetapkan dengan nilai 0.3. Rata-rata akurasi validasinya adalah 83% sehingga dapat dikatakan pelatihan model yang dikembangkan tidak mengalami overfitting. Adapun hasil pengujian dengan data baru mencapai akurasi sebesar 90%. Selanjutnya parameter model terbaik disematkan ke dalam aplikasi yang berjalan di sistem operasi berbasis Android, namun fungsionalitas serta kinerja UX/UI dari aplikasi perlu ditingkatkan untuk memfasilitasi model agar dapat digunakan secara sempurna.

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“…Habib used deep learning algorithms to classify the disease examination images from left-behind children in remote areas [6]. Ji's research found that the clinical diagnosis of keratitis is highly dependent on medical images, so he proposed the use of deep learning algorithms for the automatic diagnosis of keratitis [7]. Yang proposed an improved algorithm based on a deep learning network to remove road image redundancy.…”

Section: Introductionmentioning

confidence: 99%

Strategies for Environmental Protection and Optimization of Ecological Business Economic Growth from the Perspective of Sustainable Development

2023

Sustainability

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The concept of ecological commercial economy refers to the use of ecological economics principles and system engineering methods to change production and consumption patterns within the scope of the carrying capacity of the ecosystem in order to tap into all of the available resource potential. It develops some economically developed and ecologically efficient industries and builds a culture with reasonable systems, a harmonious society, and a healthy ecological environment. This paper aims to use deep learning algorithms to study environmental protection and the optimization of ecological business economic growth from the perspective of sustainable development. In this regard, this paper proposes a theoretical model of environmental regulation, which aids in the study of the sustainable development of the ecological economy. Through experimental analysis, this study determined that the non-renewable resources of the two cities designated M and N dropped from 82% and 99% in 2017 to 78% and 79% in 2021, a decrease of 3% and 20%, respectively. This shows that the non-renewable resources of the four cities in area A generally showed a downward trend. The experimental results show that the deep learning theory and the environmental regulation model play a specific and effective role in the researching of the ecological business economy.

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An Image Diagnosis Algorithm for Keratitis Based on Deep Learning

Cited by 5 publications

References 28 publications

Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

Implementasi Pendekatan Algoritma Deep Learning CNN untuk Identifikasi Citra Pasien Keratitis

Strategies for Environmental Protection and Optimization of Ecological Business Economic Growth from the Perspective of Sustainable Development

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