Heart Disease Prediction Using Retinal Images: A Deep Learning Approach

Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus Photographs

Identification of retinal diseases is a test for the ophthalmologists as the anomalies are just unmistakable at the beginning period. Early detection of these diseases can avoid lasting vision misfortune. Dealing with a lot of retinal images and location of variations from the norm because of these infections is difficult just as tedious. In this work, the deep learning algorithm has proposed to check the abnormality condition of retina with the help of retinal fundus images. In deep leaning a training set is produced with features of variations from the norm present in the retinal images and the infection the retina is experiencing. The deep Convolutional Neural Network (CNN) classifier predicts the infection for every retinal images in the wake of social event the learning from training the set. The rightness of desire is resolved to evaluate the viability of the classifier. The proposed technique was executed in MATLAB and assessed both normal and abnormal diabetic retinopathy retinal images of IDRID, ROC, and local datasets. The proposed technique has gotten better execution measurements, for example, sensitivity of 98.2%, Specificity of 98.45%, accuracy of 98.56% and average area under receiver operating characteristics of 0.9 when contrasted with different conditions of the workmanship strategies.

- This paper presents a comparative analysis between a traditional convolutional neural network (CNN) model and a hybrid ResNet50 + AdaBoost architecture for non-invasive cardiovascular risk prediction using retinal fundus images. The baseline model, trained on a small dataset for binary classification, achieves reasonable accuracy but lacks scalability and granularity. In contrast, the proposed model leverages deep residual learning for feature extraction and ensemble-based classification to predict five distinct cardiovascular risk levels. Evaluated on a class-balanced dataset of over 11,500 images, the hybrid model achieved a testing accuracy of 91.48% and a macro F1-score of approximately 0.93. The system also integrates data visualization and database logging to support clinical explainability. The findings demonstrate the proposed method’s superiority in risk stratification, robustness, and deployment readiness for real-world applications. Key Words: Retinal Imaging, Cardiovascular Risk Prediction, ResNet50, AdaBoost, Deep Learning, Ensemble Learning, Fundus Images, Heart Attack Classification

Heart disease is one of the diseases that exposes high mortality worldwide. This conventional way of predicting heart disease is usually expensive, time-consuming, and prone to human error. Early detection of heart disease is important as it helps to prevent deaths caused by this disease. Machine learning utilization as the non-invasive means for predicting heart disease is considered as a fast and affordable method to prevent the fatality of heart disease. This work aims at utilizing Convolutional neural network (CNN) to enhance the performance of an Arrhythmia prediction model. We have built an Arrythmia prediction model using neural networks comprising multiple convolutional layers and maxpooling layers. Our proposed model is trained using the MIT-BIH Arrhythmia dataset. The model performance has been evaluated and the model achieves 98.43% of performance accuracy

The structure of the optic disc is impacted by the devastating eye disease glaucoma. Most doctors rely on viewing and screening the retinal fundus images to detect it. Identifying the optic disc is a crucial stage for automated diagnosis of glaucoma. This study presents a transfer learning-based approach to locate the optic disc in the retinal fundus image. The fundus images are categorized into four distinct classes according to the positions of optic disc to learn the in-depth specific features.Based on the YOLOv3 model, the optic disc detection method is then fine-tuned using the specialised image dataset. The model involved is trained and evaluated on 2178 retinal fundus images obtained from database at a nearby hospital and publically accessible datasets. In 2103 of the retinal fundus images, the suggested approach successfully detected the optical disc, yielding an average accuracy of 96.56%.

Early detection of heart disease is critical to the patient's survival. An electrocardiogram (ECG) is a test that analyses heartbeat variations. ECG is a test that checks on how your heartbeats vary. Various cardiac diseases can be detected by the deviation of signals from the typical sinus rhythm as well as from mere anomalies. The ECG signal carries minor amplitude variation, thus can cause errors as it may be difficult to make a diagnosis on the cardiac conditions. The only way to preserve the human lives is by the very accurate recognition method. The ECG signals are utilized in an appropriate and accurate way for classifying and predicting the heart diseases through a proposed study in this study. In the study, Convolutional Neural networks (CNN), Visual Geometry Group (VGG) and Logistic Regression (LR) were employed to predict the heart diseases; the results proved out robust and finally, ensemble approaches were developed based on the combination of CNN, VGG, LR with Bidirectional Recurrent Neural Network(BRNN), Gated Recurrent unit, and Long Short term memory which are used to predict heart diseases and performance of each as discussed.

Retinal fundus images play a crucial role in the early detection of eye problems, aiding in timely diagnosis and treatment to prevent vision loss or blindness. With advancements in technology, Convolutional Neural Network (CNN) algorithms have emerged as effective tools for recognition, delineation, and classification tasks. This study proposes a comprehensive review of CNN algorithms used for retinal fundus image segmentation and classification. Our review follows a systematic approach, exploring diverse repositories to identify studies employing CNN to segment and classify retinal fundus images. Utilizing CNNs in the segmentation and classification of retinal fundus images can enhance the precision of segmentation outcomes and alleviate the sole dependence on human experts. This approach enables more accurate segmentation results, reducing the burden on human experts. A total of sixty-two studies are included in our review, analyzing aspects such as database usage and the advantages and disadvantages of the methods employed. The review provides valuable insights, limitations, observations, and future directions in the field. Despite certain limitations, the findings indicate that CNN algorithms consistently achieve high accuracies. The comprehensive examination of the included studies sheds light on the potential of CNN in retinal fundus image analysis.

PurposeTo propose a novel deep learning–based methodology for drusen detection and quantification in early age-related macular degeneration (AMD) using retinal multispectral images. The retinal multispectral images highlight features in several nonoverlapping spectral bands that the deep learning models leverage for automatic drusen detection and quantification in dry AMD.MethodsThe proposed novel methodology comprises quality assessment of retinal images, region of interest extraction, drusen segmentation, and drusen quantification stages. Different deep learning models (such as UNet++ convolutional neural network with EfficientNetV2 encoder) have been implemented for these stages. A total of 170 drusen and 150 nondrusen retinal images (single eye) were split into four training and validation data sets to analyze the performance of a deep learning model for drusen segmentation.ResultsThe proposed methodology achieved an average score, recall, and precision of 0.691, 0.668, and 0.776, respectively, across all four validation sets. This work also analyzed the performance of the proposed deep learning model for discriminating drusen and drusen-like lesions, achieving a pixel-wise segmentation accuracy of 99.998%. The number and the diameter of the detected drusen were also computed. A Dice score distribution for drusen segmentation with different numbers and sizes of drusen per eye is also shown.ConclusionsThis work demonstrates that deep learning models applied to retinal multispectral images can provide accurate and clinically significant drusen segmentation and quantification, thereby facilitating early detection, longitudinal monitoring, and reduction of the risk of vision loss from AMD.Translational RelevanceDeep learning–assisted detection of drusen from multispectral retinal images will refine and improve clinical diagnosis of early nonexudative age-related macular degeneration.

BackgroundBrain volume measurement serves as a critical approach for assessing brain health status. Considering the close biological connection between the eyes and brain, this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata, and to offer a cost-effective approach for assessing brain health.MethodsBased on clinical information, retinal fundus images, and neuroimaging data derived from a multicenter, population-based cohort study, the KaiLuan Study, we proposed a cross-modal correlation representation (CMCR) network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects. Specifically, individual clinical information, which has been followed up for as long as 12 years, was encoded as a prompt to enhance the accuracy of brain volume estimation. Independent internal validation and external validation were performed to assess the robustness of the proposed model. Root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.ResultsThe proposed framework yielded average RMSE, PSNR, and SSIM values of 98.23, 35.78 dB, and 0.64, respectively, which significantly outperformed 5 other methods: multi-channel Variational Autoencoder (mcVAE), Pixel-to-Pixel (Pixel2pixel), transformer-based U-Net (TransUNet), multi-scale transformer network (MT-Net), and residual vision transformer (ResViT). The two- (2D) and three-dimensional (3D) visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images. Thus, the CMCR framework accurately captured the latent structural correlations between the fundus and the brain. The average difference between predicted and actual brain volumes was 61.36 cm3, with a relative error of 4.54%. When all of the clinical information (including age and sex, daily habits, cardiovascular factors, metabolic factors, and inflammatory factors) was encoded, the difference was decreased to 53.89 cm3, with a relative error of 3.98%. Based on the synthesized brain MR images from retinal fundus images, the volumes of brain tissues could be estimated with high accuracy.ConclusionsThis study provides an innovative, accurate, and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.Trial registration No. NCT05453877 (https://clinicaltrials.gov/).Supplementary InformationThe online version contains supplementary material available at 10.1186/s40779-025-00630-2.

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. We have been studying an automated scheme for detection of a retinal nerve fiber layer defect (NFLD), which is one of the earliest signs of glaucoma on retinal fundus images. In our previous study, we proposed a multi-step detection scheme which consists of Gabor filtering, clustering and adaptive thresholding. The problems of the previous method were that the number of false positives (FPs) was still large and that the method included too many rules. In attempt to solve these problems, we investigated the end-to-end learning system without pre-specified features. A deep convolutional neural network (DCNN) with deconvolutional layers was trained to detect NFLD regions. In this preliminary investigation, we investigated effective ways of preparing the input images and compared the detection results. The optimal result was then compared with the result obtained by the previous method. DCNN training was carried out using original images of abnormal cases, original images of both normal and abnormal cases, ellipse-based polar transformed images, and transformed half images. The result showed that use of both normal and abnormal cases increased the sensitivity as well as the number of FPs. Although NFLDs are visualized with the highest contrast in green plane, the use of color images provided higher sensitivity than the use of green image only. The free response receiver operating characteristic curve using the transformed color images, which was the best among seven different sets studied, was comparable to that of the previous method. Use of DCNN has a potential to improve the generalizability of automated detection method of NFLDs and may be useful in assisting glaucoma diagnosis on retinal fundus images.

Diabetic retinopathy (DR) is a complication of retinal related common micro vascular disease occurred in diabetic patients. It is the one of the major causes of adult blindness in the worldwide. DR is indicated by the presence of hard exudates, soft exudates, haemorrhages, the growth of new blood vessels and foveal avascular zone (FAZ) enlargement. The objective of this work is to detect FAZ area in the colour retinal fundus images. In retinal fundus images, FAZ is located in the centre of macula, which is usually capillary-free. The detection of FAZ is an important step in the assessment of retinal images for visual related pathology detection. Retinal images are taken from Messidor database consisting of 59 retinal images. The proposed method has four main steps, i.e. pre-processing, blood vessels extraction, FAZ detection, and validation test. In the pre-processing step, morphological operation followed by contrast stretching is used to enhance and improve image quality. In other hand, matched filter and local entropy thresholding are applied to extract retinal blood vessels. FAZ area is detected by connecting retinal vessel endpoints. Finally, validation test is conducted by calculating the correlation between polygon area as detected FAZ by proposed method and circular area as labelled FAZ by ophthalmologists. The result achieves the correlation coefficient of 0.912 indicating that the proposed method has significant correlation with the method of the ophthalmologists.

Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study.

Accurate retinal vessel segmentation plays a vital role in the early detection and diagnosis of ophthalmologic dis- eases such as diabetic retinopathy, glaucoma, and hypertension. Traditional image processing and machine learning techniques often struggle to capture the fine vessel structures and complex variations present in retinal fundus images. To overcome these limitations, this study proposes a hybrid deep learning framework that combines Convolutional Neural Networks (CNNs) and Gen- erative Adversarial Networks (GANs) for precise retinal vessel segmentation. In the proposed method, the CNN component serves as the feature extractor, learning multi-scale spatial and contextual features from retinal images, while the GAN framework enhances segmentation accuracy by employing a discriminator network that distinguishes between real and generated vessel maps. This adversarial learning setup encourages the generator to produce vessel segmentations that closely resemble ground truth images. Additionally, preprocessing techniques such as contrast enhancement and noise reduction are applied to improve the visibility of retinal structures, and post-processing steps refine the vessel boundaries for higher accuracy. Experimental results on publicly available retinal image datasets, such as DRIVE and STARE, demonstrate that the pro- posed GAN-CNN model outperforms conventional CNN-based segmentation methods in terms of accuracy, sensitivity, specificity, and Dice coefficient. The system achieves robust performance even under varying illumination and noise conditions. These findings indicate that integrating adversarial learning with convolutional feature extraction can significantly enhance retinal vessel segmentation, paving the way for more efficient and automated retinal disease screening systems

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

This study focuses on detecting and classifying neovascularization caused by proliferative diabetic retinopathy (PDR) in retinal fundus images. Image processing methods were applied to detect retinal vessels. A fractal analysis approach based on the box-counting method was used to quantify vascular patterns in normal and abnormal cases showing neovascularization near the optic disk (NVD). Ten images including five normal cases and five neovascularization cases were analyzed. The mean fractal dimension obtained for the NVD cases was 1.66 compared to the mean value of 1.52 for the normal cases. The statistical significance of the difference was high, with a p-value of 0.0088. The results show promise for use in detecting neovascularization in retinal images caused by PDR.