Classification of Retinal Image for Early Detection of Diabetic Retinopathy Using Deep Learning

Abstract: Diabetic retinopathy is a disease which cause of blindness due to diabetes. For this reason, it is very important to detect diabetic retinopathy in early stages. A deep learning-based approach is used for the early detection of diabetic retinopathy from retinal images. The proposed approach consists of two steps. In the first stage, pre treatments were performed to remove retinal images from different data sets and standardize them to size. In the second stage, classification is done by the help of Convolutional Neural Network using deep learning algorithm and 98.5% success is achieved. The difference of this technique from similar studies is that instead of creating the feature set manually as in traditional methods, the deep learning network automatically constructs and trained by using CPU and GPU in a very short time. Keywords: CNN, Early detection, Artificial intelligence, Deep learning, Machine-learning, Fundus Image, Optical coherence tomography, Ophthalmology.

Download Full-text

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

BMC Nephrology ◽

10.1186/s12882-021-02421-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tianqi Tu ◽

Xueling Wei ◽

Yue Yang ◽

Nianrong Zhang ◽

Wei Li ◽

...

Keyword(s):

Deep Learning ◽

Renal Biopsy ◽

Membranous Nephropathy ◽

Learning Algorithm ◽

Hyperspectral Imagery ◽

Chinese Patients ◽

Support Vector ◽

Deep Learning Algorithm ◽

The Difference ◽

Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

Download Full-text

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

Scientific Reports ◽

10.1038/s41598-020-72143-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Guido de Jong ◽

Elmar Bijlsma ◽

Jene Meulstee ◽

Myrte Wennen ◽

Erik van Lindert ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Normal Brain ◽

Healthy Controls ◽

Learning Network ◽

Deep Learning Algorithm ◽

Anterior Plagiocephaly ◽

Deep Learning Network ◽

Shape Data ◽

Cranial Shape

Abstract Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly. In order to acquire cranial shape data, 3D stereophotographs were made during routine pre-operative appointments of scaphocephaly (n = 76), trigonocephaly (n = 40) and anterior plagiocephaly (n = 27) patients. 3D Stereophotographs of healthy infants (n = 53) were made between the age of 3–6 months. The cranial shape data was sampled and a deep learning network was used to classify the cranial shape data as either: healthy control, scaphocephaly patient, trigonocephaly patient or anterior plagiocephaly patient. For the training and testing of the deep learning network, a stratified tenfold cross validation was used. During testing 195 out of 196 3D stereophotographs (99.5%) were correctly classified. This study shows that trained deep learning algorithms, based on 3D stereophotographs, can discriminate between craniosynostosis subtypes and healthy controls with high accuracy.

Download Full-text

Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review

Journal of Ophthalmology ◽

10.1155/2020/8841927 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Aan Chu ◽

David Squirrell ◽

Andelka M. Phillips ◽

Ehsan Vaghefi

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Learning Algorithm ◽

Learning Algorithms ◽

Validation Dataset ◽

Cochrane Library ◽

Clinical Implementation ◽

Diabetic Retinopathy Screening ◽

Deep Learning Algorithm ◽

Retinopathy Screening

This systematic review was performed to identify the specifics of an optimal diabetic retinopathy deep learning algorithm, by identifying the best exemplar research studies of the field, whilst highlighting potential barriers to clinical implementation of such an algorithm. Searching five electronic databases (Embase, MEDLINE, Scopus, PubMed, and the Cochrane Library) returned 747 unique records on 20 December 2019. Predetermined inclusion and exclusion criteria were applied to the search results, resulting in 15 highest-quality publications. A manual search through the reference lists of relevant review articles found from the database search was conducted, yielding no additional records. A validation dataset of the trained deep learning algorithms was used for creating a set of optimal properties for an ideal diabetic retinopathy classification algorithm. Potential limitations to the clinical implementation of such systems were identified as lack of generalizability, limited screening scope, and data sovereignty issues. It is concluded that deep learning algorithms in the context of diabetic retinopathy screening have reported impressive results. Despite this, the potential sources of limitations in such systems must be evaluated carefully. An ideal deep learning algorithm should be clinic-, clinician-, and camera-agnostic; complying with the local regulation for data sovereignty, storage, privacy, and reporting; whilst requiring minimum human input.

Download Full-text

A Deep Learning Model for Screening Type 2 Diabetes from Retinal Photographs

10.1101/2021.06.29.21259606 ◽

2021 ◽

Author(s):

Jae-Seung Yun ◽

Jaesik Kim ◽

Sang-Hyuk Jung ◽

Seon-Ah Cha ◽

Seung-Hyun Ko ◽

...

Keyword(s):

Type 2 Diabetes ◽

Deep Learning ◽

Learning Algorithm ◽

Learning Model ◽

Retinal Images ◽

Uk Biobank ◽

Non Invasive ◽

Deep Learning Algorithm ◽

Deep Learning Model

Objective: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images. Research Design and Methods: The deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF model. Results: When predicting TRFs with the deep learning algorithm, the areas under the curve (AUCs) obtained with the validation set for age, sex, and HbA1c status were 0.931 (0.928-0.934), 0.933 (0.929-0.936), and 0.734 (0.715-0.752), respectively. When predicting type 2 diabetes, the AUC of the composite logistic model using non-invasive TRFs was 0.810 (0.790-0.830), and that for the deep learning model using only fundus images was 0.731 (0.707-0.756). Upon addition of TRFs to the deep learning algorithm, discriminative performance was improved to 0.844 (0.826-0.861). The addition of the algorithm to the TRFs model improved risk stratification with an overall NRI of 50.8%. Conclusions: Our results demonstrate that this deep learning algorithm can be a useful tool for stratifying individuals at high risk of type 2 diabetes in the general population.

Download Full-text

Research on Network Security Application Based on Deep Learning

CONVERTER ◽

10.17762/converter.235 ◽

2021 ◽

pp. 598-605

Author(s):

Zhao Jianchao

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Rapid Development ◽

Internet Security ◽

Detection Accuracy ◽

Data Sets ◽

Learning Technology ◽

Data Set ◽

Deep Learning Algorithm ◽

Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

Download Full-text

Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834459 ◽

2021 ◽

Vol 8 (3) ◽

pp. 619

Author(s):

Candra Dewi ◽

Andri Santoso ◽

Indriati Indriati ◽

Nadia Artha Dewi ◽

Yoke Kusuma Arbawa

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Color Space ◽

Training Data ◽

Color Spaces ◽

Deep Learning Algorithm ◽

Optimal Accuracy

Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit diabetic retinophaty. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi diabetic retinophaty adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra fundus retina dan algoritme Convolutional Neural Network (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna. AbstractIncreasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.

Download Full-text

Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

10.21203/rs.3.rs-30329/v3 ◽

2021 ◽

Author(s):

Tianqi Tu ◽

Xueling Wei ◽

Yue Yang ◽

Nianrong Zhang ◽

Wei Li ◽

...

Keyword(s):

Deep Learning ◽

Renal Biopsy ◽

Membranous Nephropathy ◽

Learning Algorithm ◽

Hyperspectral Imagery ◽

Chinese Patients ◽

Support Vector ◽

Deep Learning Algorithm ◽

The Difference ◽

Complex Deposition

Abstract Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

Download Full-text

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma

Journal of Clinical Oncology ◽

10.1200/jco.19.02031 ◽

2020 ◽

Vol 38 (12) ◽

pp. 1304-1311 ◽

Cited By ~ 14

Author(s):

Benjamin H. Kann ◽

Daniel F. Hicks ◽

Sam Payabvash ◽

Amit Mahajan ◽

Justin Du ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Deep Learning ◽

Lymph Nodes ◽

Head And Neck ◽

Learning Algorithm ◽

Data Sets ◽

Diagnostic Ability ◽

Algorithm Performance ◽

Extranodal Extension ◽

Deep Learning Algorithm

PURPOSE Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians. METHODS We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists. RESULTS A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists’ AUCs of 0.70 and 0.71 ( P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 ( P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance. CONCLUSION Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.

Download Full-text