Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Thomas, G. Arun Sampaul; Robinson, Yohan; Julie, E. Golden; Shanmuganathan, Vimal; Nam, Yunyoung; Rho, Seungmin

doi:10.20944/preprints202005.0493.v1

Cited by 11 publications

(6 citation statements)

References 33 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It leads to offer tools for automated learning from the history of data and developing models to discover the outcomes of future scenarios. There are the various tools for data mining machine learning algorithms to identify and predict the various disease in terms of regression, decision tree and Bayesian network [17]. Finding of a disease, needed different test results in variety of scenarios with respect to the particular patient.…”

Section: Literature Surveymentioning

confidence: 99%

AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes

Jackins¹,

Vimal²,

Kaliappan³

et al. 2020

J Supercomput

217

View full text Add to dashboard Cite

Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are only utilized for analysis by a doctor who then ascertains the disease using his/her personal medical expertise. The artificial intelligence has been used with Naive Bayes classification and random forest classification algorithm to classify many disease datasets like diabetes, heart disease, and cancer to check whether the patient is affected by that disease or not. A performance analysis of the disease data for both algorithms is calculated and compared. The results of the simulations show the effectiveness of the classification techniques on a dataset, as well as the nature and complexity of the dataset used.

show abstract

Section: Literature Surveymentioning

confidence: 99%

AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes

Jackins¹,

Vimal²,

Kaliappan³

et al. 2020

J Supercomput

217

View full text Add to dashboard Cite

show abstract

“…Based on this discussion, it can be concluded that it is difficult to manually detect retinal eye diseases from OCT images, as it is a complicated and laborious task that can now be automated through the use of deep learning. Although various deep learning-based automated methods have been presented in the literature, the majority of them employ only deep CNN models and do not incorporate any feature selection techniques [ 40 ]. The proposed selection-based approach, which employs the modified ResNet-50, InceptionV3, and DenseNet-201 models, provides a reliable method for early and precise detection of various retinal diseases.…”

Section: Discussionmentioning

confidence: 99%

Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization

Khan

Aziz

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

show abstract

“…Penambangan data adalah teknik mengekstraksi data dari basis data besar menggunakan algoritme khusus untuk mengidentifikasi pola dan tren yang berguna. [4] Penambangan data adalah teknik analisis data yang secara otomatis dan cepat mengeksplorasi dan menemukan pola dan informasi tersembunyi dalam data [5] neural network adalah Neural network adalah suatu model matematis yang terinspirasi dari struktur dan fungsi otak manusia, dan digunakan untuk mempelajari pola-pola kompleks dalam data [6] Pada penelitian serupa [7] membahas tentang penerapan data mining dengan dengan Neural Network Backprogation dapat digunakan untuk untuk memprediksi harga saham pada tiga golongan bedasarkan kapitalisasinya. Pada penelitian [8] penerapan data mining dengan menggunakan Artificial Neural Network digunakan untuk untuk memprediksi harga saham BCA Tbk.…”

Section: Pendahuluanunclassified

Penerapan Algoritme Backprogation Neural Network untuk Peramalan Harga Saham Alphabet Inc

Andhika Arethuza Ari,

Deni Mahdiana

2023

KRESNA

View full text Add to dashboard Cite

Saham adalah bentuk investasi yang populer dikalangan masyarakat, pada tahun 2022 Jumlah investor di pasar modal terus bertambah seiring dengan tumbuhnya kesadaran masyarakat untuk berinvestasi saham yang didukung oleh maraknya teknologi digital. Berdasarkan data Pusat Kustodian Efek Indonesia (KSEI), hingga Agustus 2022, jumlah investor di pasar modal telah menembus 9,54 juta investor. Banyak golongan investor yang berinvestasi di pasar saham dengan harapan menghasilkan keuntungan. Alphabet Inc. merupakan salah satu perusahaan teknologi terbesar di dunia yang terdaftar di bursa saham Amerika Serikat. Perusahaan ini terkenal dengan produk-produknya seperti Google, YouTube, dan Android. Sebagai perusahaan publik, harga saham Alphabet Inc. Perusahaan ini didirikan pada tahun 2015 setelah Google melakukan restrukturisasi dan mengubah nama perusahaannya menjadi Alphabet Inc. Saham Alphabet Inc. terdaftar di bursa saham Nasdaq dengan kode saham GOOGL dan merupakan salah satu saham yang paling banyak diperdagangkan di pasar saham Amerika Serikat. Penelitian ini bertujuan untuk mengembangkan model peramalan berdasarkan metode Backprogation Neural Network untuk memprediksi harga saham Alphabet Inc.. Hasil penelitian ini mendapatkan bahwa peramalan menggunakan algoritme Bacprogation Neural Network dengan split data 60 : 40 menghasilkan hasil peramalan yang baik karena menciptakan nilai RMSE yang rendah yaitu 0.462, serta. Serta dengan menggunakan algoritme backprogation menghasilkan Threshold sebesar 0.190.

show abstract

Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Cited by 11 publications

References 33 publications

AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes

AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes

Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization

Penerapan Algoritme Backprogation Neural Network untuk Peramalan Harga Saham Alphabet Inc

Contact Info

Product

Resources

About