Diabetic Retinopathy (DR) is one of the leading causes of vision impairment and blindness globally, necessitating early and accurate detection for timely clinical intervention. This paper proposes NGCF-RVFL, a novel Computer-aided-diagnosis system for multi-grade DR detection from retinal fundus images. The working of this system begins with an enhanced preprocessing pipeline that includes median filtering, Gaussian filtering, and Contrast-limited adaptive histogram equalization to reduce noise and improve contrast of the fundus images. Next, we introduce an adaptive image augmentation technique to address the issue of class imbalance. Minority class samples are increased using an augmentation that adapts the size of majority class samples. After that, we propose a Next Generation Convolutional Feature (NGCF) based on the fine-tuned ConvNeXt architecture, consisting of a hierarchical design with four feature extraction stages utilizing depthwise separable convolutions. The NGCF feature effectively encodes intricate retinal structures and disease patterns crucial for accurate DR grading. Further, the discriminative analysis with Principal Component Analysis confirms the significance and effectiveness of the extracted NGC feature in representing relevant retinal information. Furthermore, a lightweight network, Random Vector Functional Link (RVFL), is employed to evaluate the grade-wise detection performance of the proposed NGCF feature. Unlike traditional iterative learning models, the RVFL utilizes a single-pass training mechanism, significantly reducing computation time while maintaining high detection performance. Finally, we evaluate the effectiveness and detection performance of the NGCF feature on other machine learning classifiers such as Support vector machine, Multilayer perceptron, Random forest, and Decision tree. Comprehensive experiments on a benchmark dataset demonstrate that NGCF-RVFL achieves competitive scores across all DR grades with minimal training time, outperforming the state-of-the-art approaches.

Content-based retinal image analysis plays a crucial role in the early diagnosis of ocular diseases. In this study, we proposed a novel approach for efficient content-based retinal image retrieval and Diabetic Retinopathy (DR) detection using variants of local texture features derived from Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Gray Level Co-occurrence Matrix (GLCM). The methodology begins with meticulous image preprocessing to enhance feature extraction, followed by the extraction of LBP, LTP, and GLCM features, which capture intricate texture patterns and enrich the feature space for robust analysis. Subsequently, we trained machine learning models, including Support Vector Machine (SVM), Decision Tree, and Random Forest, on the extracted features to effectively retrieve retinal images and detect DR. A comparative analysis between preprocessed and raw images highlights the impact of preprocessing techniques on performance. A key innovation of this study lies in the fusion of multiple texture-based features, creating a comprehensive representation that integrates high-level semantic information with fine-grained local patterns. This hybrid approach enhances the system’s capability to handle diverse retinal image variations, leading to improved retrieval accuracy and robustness. Further, a metaheuristic approach for feature selection and optimization is employed, comparing Differential Evolution, Genetic Algorithm, and Particle Swarm Optimization to identify the most effective features for retrieval. Differential Evolution achieved the highest precision of 90.67 % for retrieving the top 10 relevant images. The proposed hybrid approach demonstrates the effectiveness of integrating classical image analysis methods with machine learning for DR detection and content-based image retrieval. This research contributes to precision medicine and healthcare innovation by advancing ML-driven retinal image analysis.

Diabetic Retinopathy (DR) is a leading cause of vision loss among diabetic patients, and its early detection is essential to prevent irreversible blindness. In this work, we propose an automated framework for DR grade detection that combines deep-inceptional features with a Support Vector Machine (SVM) classifier. Retinal images are first preprocessed using median filtering, Gaussian filtering, and Contrast-Limited Adaptive Histogram Equalization to enhance contrast and highlight retinal lesions. Further, to address class imbalance and improve model generalizability, adaptive data augmentation is applied. After that, Deep-inceptional features are extracted from the augmented fundus images using a fine-tuned InceptionV3 network, which effectively captures hierarchical and discriminative information, including fine-grained lesion structures and retinal vessel patterns. The extracted features are then employed to train a SVM classifier using three distinct kernel functions: linear, polynomial, and radial basis function (RBF). Experimental evaluations on a benchmark dataset demonstrate that the SVM with the RBF kernel achieves better performance compared to the other kernel configurations. The proposed method outperforms several existing approaches in binary and multi-grade DR detection, showcasing its potential for effective and scalable DR screening.

Skin cancer poses a significant public health challenge, where computer-based automated systems can play an important role in easing the growing burden of this disease. Accurate segmentation of skin lesions from dermoscopic images is a vital step toward effective diagnosis. However, this task remains challenging due to variations in lesion shape and contrast, the presence of natural and artificial artifacts such as hair and air bubbles, image noise, and inconsistencies in image acquisition settings. This study presents an Attention Residual U-Net model for semantic segmentation of skin lesions in dermoscopic images. The architecture employs skip connections to preserve spatial details lost during encoding. The integration of attention mechanisms and residual skip connections enables the network to capture fine-grained contextual and spatial details, thereby enhancing lesion boundary detection and segmentation. Evaluation metrics such as Jaccard Coefficient, Dice Coefficient and its loss function are used to assess the models' performance. Additionally, the study examines the influence of various hyperparameters, noise factors, and preprocessing strategies on performance. Experimental results on the ISIC (International Skin Imaging Collaboration) 2018 dataset demonstrate that the proposed model surpasses existing approaches. The model achieved a segmentation accuracy of 94.8%, and maintained strong robustness under Gaussian and salt-and-pepper noise with accuracies of 89.9% and 91.1%, respectively.

Diabetic Retinopathy (DR) is a disease of the retina caused by diabetes mellitus that threatens vision. DR can progress without giving any signs until it causes sudden loss of vision. So, early detection of DR is crucial. Manual diagnosis of DR demands a high level of expertise and efforts from trained ophthalmologists, which is costly, time-consuming, and prone to misdiagnosis. The availability of trained ophthalmologists is significantly less, so an automated approach is required to efficiently detect the disease in its early stages with better diagnosis and less expense. Computer-Aided-Diagnosis (CAD) systems play a crucial role in assisting ophthalmologists and retinal experts in early disease detection. Using computer vision, image processing, and artificial intelligence techniques, including machine learning and deep learning, has shown significant promise in medical imaging for developing automated diagnostic tools. This paper reviews various datasets of retinal fundus images and explores corresponding methodologies for DR detection. Over the past few years, numerous studies have focused on DR detection, encompassing retinal image segmentation, classification, and detection. This paper provides a comprehensive analysis of these studies, identifying gaps and challenges while offering insights into future directions for improving DR detection using retinal fundus imaging. Further, it emphasizes real-world deployment challenges such as interpretability, generalizability, and integration into clinical workflows.

Computer-aided diagnosis (CAD) system assists ophthalmologists in early diabetic retinopathy (DR) detection by automating the analysis of retinal images, enabling timely intervention and treatment. This paper introduces a novel CAD system based on the global and multi-resolution analysis of retinal images. As a first step, we enhance the quality of the retinal images by applying a sequence of preprocessing techniques, which include the median filter, contrast limited adaptive histogram equalization (CLAHE), and the unsharp filter. These preprocessing steps effectively eliminate noise and enhance the contrast in the retinal images. Further, these images are represented at multi-scales using discrete wavelet transform (DWT), and center symmetric local binary pattern (CSLBP) features are extracted from each scale. The extracted CSLBP features from decomposed images capture the fine and coarse details of the retinal fundus images. Also, statistical features are extracted to capture the global characteristics and provide a comprehensive representation of retinal fundus images. The detection performances of these features are evaluated on a benchmark dataset using two machine learning models, i.e., SVM and k-NN, and found that the performance of the proposed work is considerably more encouraging than other existing methods. Furthermore, the results demonstrate that when wavelet-based CSLBP features are combined with statistical features, they yield notably improved detection performance compared to using these features individually.

Accurate segmentation of retinal blood vessels is crucial for automated detection of diabetic retinopathy (DR), a leading cause of vision impairment worldwide. Although UNet and its variants are widely used for medical image segmentation, their comparative robustness under noisy image conditions remains underexplored. This study systematically evaluates five deep learning-based UNet architectures - UNet, Dense UNet, Attention UNet, Inception UNet, and Residual UNet - using three benchmark retinal image datasets. A uniform preprocessing and augmentation pipeline is applied, and all models are optimized using the Dice loss function with the Adam optimizer. Also, to simulate real-world imaging challenges and assess the noise resilience of these models, salt-and-pepper and Gaussian noise are artificially introduced. The architectures are benchmarked across multiple metrics, including accuracy, sensitivity, specificity, Dice coefficient, and Intersection over Union (IoU). Among the variants, Attention UNet consistently achieved superior performance, particularly in highlighting fine vascular structures and managing inter-image variability. These findings demonstrate that UNet-based deep learning models, especially those incorporating attention mechanisms, provide a robust and scalable approach for retinal blood vessel segmentation.

Diabetic retinopathy (DR) is a leading cause of blindness among diabetic patients worldwide. Early detection and timely intervention are crucial for preventing vision loss. In this paper, we propose a novel approach for the automated detection of diabetic retinopathy utilizing segmentation-based fractal texture analysis (SFTA) and center symmetric local binary pattern (CSLBP) features with support vector machine (SVM) classification. The proposed methodology begins with the preprocessing stage aimed at enhancing the image quality. This stage includes Gaussian and median filtering to reduce noise, contrast limited adaptive histogram equalization (CLAHE) to improve local contrast, and unsharp filtering to enhance image sharpness. Following preprocessing, we extract the SFTA features that provides a comprehensive analysis of the fractal properties of different image segments and encodes the complex geometric structures present in retinal images. Also, we extract the CSLBP features that provide discriminative texture features capturing local patterns within retinal image. The integration of SFTA and CSLBP features offers a robust representation of retinal texture characteristics, enhancing the detection accuracy of abnormalities associated with DR. We employ the SVM classifier, utilizing three different kernels: linear, quadratic, and Gaussian. This enables us to explore the effectiveness of various kernel functions in distinguishing between healthy and DR retinal images based on the extracted features. Experimental evaluation conducted on a publicly available dataset demonstrates the effectiveness of the proposed approach, achieving superior performance compared to state-of-the-art methods.

Diabetic retinopathy (DR) is an eye disease involving retina that may develop as a complication of diabetes and can lead to significant vision impairment in its early stages and may later cause even the blindness. Diagnosis of DR is typically accomplished by analysing non-invasive fundus images. This paper introduces a new method for detecting DR, which comprises the extraction of Local binary patterns (LBP) and statistical features from the fundus images, followed by the training of Machine learning (ML) models. The performance of the introduced features for detecting the DR is evaluated on a benchmark dataset. It is found that the combination of LBP and statistical features achieves significantly encouraging detection performance on SVM, k-NN, ANN, and Logistic regression (LR) classifiers, as compared to using the features individually. Among these classifiers, the detection performance of SVM is quite encouraging than others.

Diabetic Retinopathy (DR) is one of the common issues of diabetic Mellitus that affects the eyesight of humans by causing lesions in their retinas. DR is mainly caused by the damage of blood vessels in the tissue of the retina, and it is one of the leading causes of visual impairment globally. It can even cause blindness if not detected in its early stages. To reduce the risk of eyesight loss, early detection and treatment are pretty necessary. The manual process by ophthalmologists in detection DR requires much effort and time and is costly also. Many computer-based techniques reduce the manual effort, and deep learning is used more commonly in medical imaging. This chapter will discuss deep learning and how it is helpful in the early detection and classification of DR by reviewing some latest state-of-art methods. There are various datasets of colour fundus images available publically, and we have reviewed those databases in this chapter.