Sistem Deteksi Level Diabetic Retinopathy Melalui Citra Fundus Mata dengan Menggunakan Metode CNN (Convolutional Neural Network)

Diabetes merupakan salah satu penyakit yang memiliki dampak serius pada kesehatan mata, terutama pada kondisi yang dikenal sebagai Diabetic Retinopathy (DR). DR dapat menyebabkan kerusakan retina dan berpotensi menyebabkan kehilangan penglihatan. Oleh karena itu, deteksi dini dan pemantauan berkala sangat penting. Penelitian ini bertujuan untuk mengembangkan sistem deteksi level Diabetic Retinopathy pada citra fundus mata menggunakan metode Convolutional Neural Network (CNN). CNN adalah salah satu teknik dalam bidang Deep Learning yang telah terbukti efektif dalam analisis citra kompleks seperti citra medis. Dataset yang digunakan adalah citra fundus mata yang bersumber dari kaggle dan telah diberi label pada setiap kelasnya. Sistem yang dibuat menggunakan software matlab yang dapat mengklasifikasikan Diabteic Retinopathy kedalam lima kelas. Hasil pengujian diperoleh hasil terbaik dengan tingkat akurasi setinggi 85%.Diabetes is one disease that has a serious impact on eye health, especially in a condition known as Diabetic Retinopathy (DR). DR can cause retinal damage and potentially lead to vision loss. Therefore, early detection and regular monitoring are essential. This study aims to develop a system for detecting  Diabetic Retinopathy levels on fundus images of the eye using the Convolutional Neural Network (CNN) method. CNN is one technique in the field of Deep Learning that has proven effective in complex image analysis such as medical images. The dataset used is an image of the fundus of the eye sourced from kaggle and has been labeled in each class. The system is made using matlab software that can classify Diabteic Retinopathy into five classes. The test results  obtained the best results with an accuracy rate as high as 85

Diabetes Retinopathy Prevalence and Risk Factors among Diabetic Patients Seen at Highland Eye Clinic Mutare Zimbabwe: A Retrospective Study

Objective: To determine the prevalence of diabetic retinopathy and its association with hypertension, age, gender, and fasting blood glucose level.Methods: This retrospective study assessed the prevalence of diabetic retinopathy with its associated risk factors on 135 diabetic patients, aged 18 years and above, visiting the Highland Eye Clinic Mutare, Zimbabwe. Data were collected on the age, sex, and type of retinopathy. Based on the identified retinopathy, subjects were divided into no retinopathy, non-proliferative diabetic renopathy, and proliferative diabetic retinopathy groups. Analysis were then performed using multivariate and univariate regression analyses to test the association between the presence of retinopathy and several risk factors, and results were presented in percentages, with P< 0.05  considered to show statistical significance.Results: The average age of the subjects this study was 60.8 ± 14 with female subjects constituted more than half of the total number of subjects (58.5%). Forty four percent were overweight (BMI 25-30), 34.8 % were obese, and the overall prevalence of diabetic retinopathy was 31.1% (non-proliferative diabetic renopathy, 20%; proliferative renopathy, 11.1%). The proportion of subjects with retinopathy increased with duration of DM, being 23.3% in those with a DM duration of less than 10 years and 46.6% in those with a DM duration of more than 10 years. Age and hypertension were significantly associated with the presence of diabetic retinopathy (P< 0.05) in univariate analysis, but no association was identified between retinopathy and fasting blood glucose (chi-square test, P =0.0965)Conclusion: The prevalence of diabetic retinopathy (DR) is high (31.1%), Non-proliferative DR is more common than the proliferative (DR). There is a strong association between diabetic retinopathy, hypertension, and age

Dilated Convolutional Neural Network for Skin Cancer Classification Based on Image Data

Skin cancer is a disorder of cell growth in the skin. Skin cancer has a big impact, causing physical disabilities that can be seen directly and high treatment costs. In addition, skin cancer also causes death if nor treated properly. Generally, dermatologists diagnose the presence of skin cancer in the human body by using the Biopsy process. In this study, the Dilated Convolutional Neural Network method was used to classify skin cancer image data. Dilated Convolutional Neural Network method is a development method of the Convolutional Neural Network method by modifying the dilation factors. The Dilated Convolutional Neural Network method is divided into two stages, including feature extraction and fully connected layer. The data used in this study is HAM1000 dataset. The data are dermoscopic image datasets which consists of 10015 images data from 7 types of skin cancer. This study conducted several experimental scenarios of changes in the value of d, which are 2,4,6, and 8 to get the optimal results. The parameters used in this study are epoch = 100, minibatch size = 8, learning rate = 0.1, and dropout = 0.5. The best results in this study were obtained with value of d=2 with the value of accuracy is 85.67% and the sensitivity is 65.48%

The diagnosis of diabetic retinopathy by means of transfer learning and fine-tuned dense layer pipeline

Diabetes is a global disease that occurs when the body is disabled pancreas to secrete insulin to convert the sugar to power in the blood. As a result, some tiny blood vessels on the part of the body, such as the eyes, are affected by high sugar and cause blocking blood flow in the vessels, which is called diabetic retinopathy. This disease may lead to permanent blindness due to the growth of new vessels in the back of the retina causing it to detach from the eyes. In 2016, 387 million people were diagnosed with Diabetic retinopathy, and the number is growing yearly, and the old detection approach becomes worse. Therefore, the purpose of this paper is to computerize the old method of detecting different classes of DR from 0-4 according to severity by given fundus images. The method is to construct a fine-tuned deep learning model based on transfer learning with dense layers. The used models here are InceptionV3, VGG16, and ResNet50 with a sharpening filter. Subsequently, InceptionV3 has achieved 94% as the highest accuracy among other models

Rapid detection of diabetic retinopathy in retinal images: a new approach using transfer learning and synthetic minority over-sampling technique

The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures