Automated Diagnostic System for Grading of Diabetic Retinopathy Stages from Fundus Images Using Texture Features

Computational methodologies and medical imaging are become an important part of real time applications. These techniques transform medicine by providing effective health care diagnosis in all major disease areas. This will allow the clinicians to understand life-saving information using less invasive techniques. Diabetes is a rapidly increasing worldwide disease that occurs when the body is unable to metabolize glucose. It increases the risk of a range of eye diseases, but the main cause of blindness associated with diabetes is Diabetic retinopathy (DR). A new feature based automated technique for diagnosis and grading of normal, Nonproliferative diabetic retinopathy (NPDR) and Proliferative diabetic retinopathy (PDR) is proposed in this paper. This method involves preprocessing of retinal images, detection of lesions, extraction of blood vessels and extraction of texture features such as local binary pattern, Laws texture energy and Fractal Dimension. These features were used for classification of DR stages by means of supervised classifiers namely Support vector machine (SVM) and Extreme Learning Machine (ELM). In this work, in addition to morphological features, statistically significant texture features were also used for classification. It was found that the average classification accuracy of 98.88%, sensitivity and specificity of 100% respectively achieved using ELM classifier with texture features. The results were validated by comparing with expert ophthalmologists. This proposed automated diagnostic system reduces the work of professionals during mass screening of DR stages

IDENTIFIKASI FASE PENYAKIT RETINOPATI DIABETES MENGGUNAKAN JARINGAN SYARAF TIRUAN MULTI LAYER PERCEPTRON

Penyakit retinopati diabetes (DR) merupakan salah satu komplikasi pada retina yang disebabkan oleh penyakit diabetes. Tingkat keparahan DR dibagi atas empat kelas yakni: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), dan macular edema (ME). Penelitian ini bertujuan untuk mengembangkan suatu metode yang dapat digunakan untuk mengidentifikasi fase retinopati diabetes. Dalam penelitian ini digunakan 97 data citra yang diekstrak menggunakan metode ekstraksi ciri gray level cooccurence matrix (GLCM). Fitur ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Fitur – fitur ini dilatih menggunakan jaringan syaraf tiruan multi layer perceptron untuk dilakukan identifikasi. Akurasi yang dihasilkan dari pendekatan ini adalah 97.73%

Intelligent non-destructive classification of josapine pineapple maturity using artificial neural network

The pineapple maturity level also referred as pineapple maturity index is based on the percentage of yellowish that appears on the pineapple’s skin. In pineapple industry to determine the level of maturity, human experts adopt methods based on their subjective assessment of skin color. To this day, the pineapple maturity sorting process is still performed manually by expert human grader. So in order to reduce errors caused by human factors, there is a need to automate this process to an automated inspection system. The matured fruit harvested for the purposes of local sale or export is complete fruit with crown, fruit body and stump. However, in determining the pineapple maturity index, the main thing to be considered is only the pineapple fruit without crown. Fruit without crown also represents the actual size of the pineapple. Therefore the percentage of yellowish must be proportional to the size of the pineapple. Having extensive search of literatures found that studies of the size of the fruit, especially pineapple are very limited and only been started in recent years. To obtain the actual size of the fruit, the detection Region of Interest (ROI) is using segmentation method called minimum symmetrical edge distance. This minimum symmetrical edge distance algorithm wills geometrical rotated the pineapple images which to align with horizontal axis. Then the shortest vertical distances of the edge is calculated and converted to a background pixel, the largest region (fruit body) is maintained and the small region (crown) was abolished. The performance of segmentation algorithms are calculated using misclassification error that provides the rate of image pixels are incorrectly misclassified into the wrong segment. The results reveal that the algorithm used to achieve overall accuracy up to 99.05%. ROI that has been identified lengthened for feature extraction on the skin color of pineapple. Statistical based features namely minimum, maximum, arithmetic average and standard deviation were extracted from each image channels within detected ROI to represent pineapple skin color's tendency and dispersion. Next, classification index to determine the pineapple maturity level has been applied which are linear classification using thresholding value and artificial neural network adopting pattern recognition method. The results show that the classification using artificial neural network (pattern recognition) involving feature vectors arithmetic average and standard deviation for all channels R, G and B give the average correct classification rate of 88.89%

Klasifikasi Citra Diabetic Retinopathy Menggunakan 3D-GLCM Projection

Penyakit diebetes melitus yang berkelanjutan akan mengakibatkan banyak komplikasi pada pasien penderita. Salah satu diantaranya adalah penyakit diabetic retinopathy. Pemeriksaan medis terhadap penderita penyakit diabetic retinopathy dilakukan pengamatan secara langsung pada citra retina menggunakan kamera fundus. Tingkat keparahan penyakit diabetic retinopathy dibagi atas empat kelas yakni: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), dan macular edema (ME). Penelitian ini bertujuan untuk mengembangkan suatu metode yang dapat digunakan untuk mengklasifikasi tingkat keparahan penyakit diabetic retinopathy berdasarkan citra retina pasien . Proses klasifikasi terhadap tingkat keparahan penyakit diabetic retinopathy dilakukan berdasarkan ciri statistik dari citra retina pasien yang diperoleh melalui proses ekstraksi ciri menggunakan metode ekstraksi 3D-GLCM Projection yang merupakan modifikasi metode 3D-GLCM. Ciri-ciri statistik tersebut kemudian dilatih menggunakan jaringan saraf tiruan dengan aturan pembelajaran backpropagation algorithm. Berdasarkan hasil pengujian yang dilakukan maka metode ini dapat melakukan klasifikasi terhadap tingkat keparahan penyakit diabetic retinopathy dengan sensitivity sebesar 100%, spesivisity sebesar 91% dan akurasi sebesar 95,83%

An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images

Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet despite its prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for assessing their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to DR severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. In this paper, we propose a machine learning system for the detection of referable DR in fundus images that is based on the paradigm of multiple-instance learning. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. Moreover, it can highlight potential image regions where DR manifests through its characteristic lesions. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance, while also producing interpretable visualizations of its predictions.Comment: 11 page

A novel few-shot classification framework for diabetic retinopathy detection and grading

Diabetes Retinopathy (DR) is a major microvascular complication of diabetes. Computer-Aided Diagnosis (CAD) tools for DR management are primarily developed using Artificial Intelligence (AI) methods, such as machine and deep learning algorithms. DR diagnostic tools have been developed in recent years using deep learning models. Thus, these models require large amounts of data for training. Consequently, these huge amounts of data are not balanced due to fewer cases in the dataset. To solve the problems associated with training models with small datasets, such as overfitting and poor approximation, this paper proposes a paradigm called Few-Shot Learning (FSL) which uses a relatively small amount of training data to train the models effectively. This paper proposes a novel prototype network, a type of FSL classification network capable of grading and detecting DR based on attention. The DRNet framework uses episodic learning to train its model on few-shot classification tasks. We developed a DRNet based on the APTOS2019 dataset for diabetic detection and grading. In the proposed network, aggregated transformations and gradient activations of classes are leveraged to design the attention mechanism to capture image representations. As a result, the system achieves 99.73 % accuracy, 99.82 % sensitivity, 99.63 % specificity in DR detection, 98.18 % accuracy, 97.41% sensitivity, and 99.55% specificity in DR grading. An analysis of objective performance metrics and model interpretation shows that the proposed model can detect DR more efficiently and grade the severity more accurately when using unseen fundus images than existing state-of-the-art methods. Therefore, this tool could help provide a second opinion to an ophthalmologist about the severity level of DR

The Transcriptional Effects of Photobiomodulation in an In Vitro Model of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common complication of diabetes mellitus and a leading cause of blindness. The pathophysiology of DR is complicated, involving inflammation, oxidative stress, retinal vascular proliferation, and vascular degeneration. Symptomatically, the growth and subsequent rupture of vessels within the frame of view leads to the development of vision loss and eventual blindness. Prior to the development of symptoms, oxidative stress involved in DR leads to the activation of the transcription factor, nuclear factor-kB (NF-kB), resulting in the excess production of vascular endothelial growth factor (VEGF) and intracellular adhesion molecule-1 (ICAM-1), proteins involved in vascular development and immune dysregulation, respectively. The most common therapeutic approach for DR utilizes anti-VEGF agents to reduce vascular proliferation. These treatments are expensive, invasive, frequently ineffective, and have numerous adverse effects, such as retinal detachment, infection, and inflammation inside the eye. A non-invasive alternative therapy is clearly needed. Photobiomodulation (PBM) using far-red to near infrared (NIR) light has been shown to reduce oxidative stress and inflammation in vitro and in vivo and is an ideal candidate for an alternative therapy. Indeed, PBM slows the progression of DR in animal models via attenuation of oxidative stress and by reducing the relative level of ICAM-1. We hypothesize that PBM will reduce the activity of NF-kB and reduce the production of VEGF and ICAM-1 in an in vitro model of DR. To test this hypothesis, we used an in vitro model system of cultured retinal Müller glial cells grown in normal (5 mM) or high (25 mM) glucose conditions for either 3 or 6 days to simulate normoglycemia and hyperglycemia. Cultures were treated with 670 nm light emitting diode (LED) (180 seconds at 25 mW/cm2; 4.5 J/cm2) or no light (sham) for 3 or 5 days. NF-kB activity and ICAM-1 concentrations were significantly increased under high glucose conditions, as measured by a dual luciferase assay or western blot, respectively. Treatment with 670 nm LED significantly reduced NF-kB activity of high glucose culture cells to values comparable to transcriptional activity measured under normoglycemic condition and decreased the level of ICAM-1. VEGF concentrations were not affected by high glucose or PBM. These data are in partial support of our central hypothesis that in an in vitro model of DR, 670 nm light will reduce activation of NF-kB, and reduce the synthesis of ICAM-1 and VEGF. The lack of an observable effect of hyperglycemia or PBM on VEGF concentrations indicates that the stimulation of VEGF secretion requires the activation of additional signaling pathways not induced by high glucose alone