Rough set based feature selection model for diabetic retinopathy classification

A Comparative Performance Evaluation of Supervised Feature Selection Algorithms on Microarray Datasets

Feature selection plays an important role in the fields of pattern recognition, data mining and machine learning. Rough set method is one of effective methods for feature selection, which can preserve the meaning of the features. Presently ant colony optimization (ACO) has been successfully applied to rough set-based feature selection, however, it has the limitations of many control parameters, premature convergence and stagnation. In this paper, we propose a novel discrete artificial bee colony algorithm to overcome these drawbacks. In our algorithm, a search mechanism centered with feature core is introduced to avoid blindly searching, in which the values of corresponding bits of these features in feature core are unchanged during the iterative process. Meanwhile, Tabu search is adopted to avoid falling into local optima for every employed bee who can not select the solution of the Tabu list in each generation. In this work, our algorithm is applied to eight datasets from UCI database and the results produced by our algorithm and some rough set-based feature selection algorithms have been compared. The results show that our algorithm outperforms the other algorithms.

Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naive Bayes (NB), Bayesian Logistic Regression (BLR), Multi-Layer Perceptron (MLP), J48, Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM), to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction. ACM CCS (2012) Classification : Computing methodologies → Machine learning → Machine learning algorithms → Feature selection; Information systems → Information retrieval → Retrieval tasks and goals → Clustering and classification; Applied computing → Operations research → Forecasting *To cite this article: S. Mohankumar and V. Balasubramanian, Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique, CIT. Journal of Computing and Information Technology , vol. 24, no. 2, pp. 181–194, 2016.

Warts are benign tumors, caused by human papillomavirus (HPV). The present study mainly emphasis on the selection of suitable methods for the removal of a common and plantar wart. There are numerous wart treatment methods for this disease, among them cryotherapy and immunotherapy are well-known approaches. Identifying the suitable wart treatment method manually is quite challenging. Moreover, existing machine learning (ML) techniques show a poor prediction accuracy towards the selection of wart treatment method, however, the prediction accuracy is not satisfactory and can be further improved. To achieve the same, the current study utilizes the advantage of fuzzy rough set based feature selection (FRFS) to generate the most optimal informative feature space, which in turn makes the ML algorithms more accurate and leads to a better prognosis. The proposed FRFS based Naive Bayes and FRFS based CART models outperform from the existing model in terms of prediction accuracy.

Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

BackgroundAccurate segmentation of the foveal avascular zone (FAZ) is valuable for retinal imaging, as FAZ alterations are key biomarkers for diabetic retinopathy (DR). This study presents an automated framework exploring the feasibility of FAZ segmentation and DR classification using optical coherence tomography angiography (OCTA) images.MethodsIn this cross-sectional study conducted at Farabi Eye Hospital, Tehran, Iran, a two-step deep learning pipeline was developed. First, a neural network combining DeepLabv3+, EfficientNetB0, Squeeze-and-Excitation (SE) blocks, and Atrous Spatial Pyramid Pooling (ASPP) was trained to segment the FAZ from superficial capillary plexus (SCP) and deep capillary plexus (DCP) OCTA slabs. Second, a GoogLeNet-based convolutional neural network (CNN) classified segmented FAZ images into binary (normal vs. DR) and three-class (normal, non-proliferative DR [NPDR], proliferative DR [PDR]) categories to differentiate DR stages based on FAZ shape characteristics. For the classification task using the deep learning-generated segmented FAZ images as input, the data was split into 70% training, 10% validation, and 20% testing, with 5-fold cross-validation to mitigate overfitting. Data augmentation and Synthetic Minority Oversampling Technique (SMOTE) were applied to improve classification performance.ResultsThe final dataset comprised 253 OCTA scans (126 SCP, 127 DCP) from 161 eyes of 161 participants (one eye per participant), with 39 normal participants (24.2%), 78 patients with NPDR (48.4%), and 44 with PDR (27.3%). The mean age was 49.7 ± 11.8 years, and 54% were male. The FAZ segmentation network achieved a Dice similarity coefficient (DSC) of 97.5% across the dataset, achieving high precision even in challenging images. The classification model, using the deep learning generated segmented FAZ images as input, reached an area under the curve (AUC) of 100% for binary classification (normal vs. DR) and 87% for three-class classification (normal, NPDR, PDR) with oversampling.ConclusionThis system, with its potential for integrating into clinical workflows, offers a promising assistive tool for clinicians, which could enable earlier and more accurate diagnosis of diabetic retinopathy from OCTA images.Clinical trial numberNot applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12886-025-04473-2.

Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal fundus images. In addition, ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions. Moreover, the fusion of two DL models namely, CapsNet and MobileNet is used for feature extraction. Further, the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm (COA). Gated Recurrent Unit (GRU) is also utilized to identify DR. The experimental results of the analysis, accomplished by ODL-FDRDC technique against benchmark DR dataset, established the supremacy of the technique over existing methodologies under different measures.

The application of Artificial Intelligence in the medical market brings up increasing concerns but aids in more timely diagnosis of silent progressing diseases like Diabetic Retinopathy. In order to diagnose Diabetic Retinopathy (DR), ophthalmologists use color fundus images, or pictures of the back of the retina, to identify small distinct features through a difficult and time-consuming process. Our work creates a novel CNN model and identifies the severity of DR through fundus image input. We classified four known DR features, including micro-aneurysms, cotton wools, exudates, and hemorrhages, through convolutional layers and were able to provide an accurate diagnostic without additional user input. The proposed model is more interpretable and robust to overfitting. We present initial results with a sensitivity of 97% and an accuracy of 71%. Our contribution is an interpretable model with similar accuracy to more complex models. With that, our model advances the field of DR detection and proves to be a key step towards AI-focused medical diagnosis.

Purpose: To develop a new domain generalization (DG) framework for diabetic retinopathy (DR) classification using Vision Transformers (VTs). Methods: A deep-learning-based prediction-softening mechanism has been used to allow self-distillation, whereby the knowledge extracted is transferred to intermediate layers. Intermediate representations are further improved with an adaptive convex combination of one-hot labels and internal predictions of the model, which helps regularize the network and counteract overfitting. Various publicly available DR datasets, such as APTOS and EYEPACS, in multi-source and single-source DG settings containing three VT backbones (DeiT, T2T-VT, and CvT) were used. Results: The proposed framework outperforms current DG methods, with better top-1 accuracy, strong calibration and predictive accuracy in unfamiliar domains. Conclusion: The Softening Predictions for Self-Distillation (SPSD)-VT framework is a robust, reliable, and generalizable framework that classifies DR and underscores the important role that domain generalization plays in medical imaging and sets a standard for future studies.

A Hybrid Diabetic Retinopathy Neural Network Model for Early Diabetic Retinopathy Detection and Classification of Fundus Images

Chapter 3 - Classification of Diabetic Retinopathy

Diabetic Retinopathy (DR) is a severe eye condition predominantly impacting people suffering from diabetes, and it stands as a major contributor to adult blindness. Traditional detection methods involving optometric physicians are laborious and time-consuming. Recent advancements in artificial intelligence (AI) enable more efficient automatic DR detection. This paper presents an Enhanced Diabetic Retinopathy Detection and Classification Model using the Whale Optimization Algorithm (EDRDCM-WOA). The EDRDCM-WOA automates DR classification by employing Gabor filtering (GF) for noise removal during preprocessing. It uses the SqueezeNet method to generate feature vectors and applies the Whale Optimization Algorithm (WOA) for optimal hyperparameter tuning, enhancing classification performance. The Kernel Extreme Learning Machine (KELM) is then used for final classification. Simulations on benchmark dataset demonstrate that the EDRDCM-WOA outperforms recent models, highlighting its effectiveness in DR detection and classification.

Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.

Diabetic retinopathy (DR) is a serious complication of diabetes that can result in vision loss if untreated, often progressing silently without warning symptoms. Elevated blood glucose levels damage the retina's microvasculature, initiating the condition. Early detection through retinal fundus imaging, supported by timely analysis and treatment, is critical for managing DR effectively. However, manually inspecting these images is a labour-intensive and time-consuming process, making computer-aided diagnosis (CAD) systems invaluable in supporting ophthalmologists. This research introduces the Fundus Imaging Diabetic Retinopathy Classification using Deep Learning and Fennec Fox Optimization (FIDRC-DLFFO) model, which automates the identification and classification of DR. The model integrates several advanced techniques to enhance performance and accuracy.1.The proposed FIDRC-DLFFO model automates DR detection and classification by combining median filtering for noise reduction, Inception-ResNet-v2 for feature extraction, and a gated recurrent unit (GRU) for classification.2.Fennec Fox Optimization (FFO) fine-tunes the GRU hyperparameters, boosting classification accuracy, with its effectiveness demonstrated on benchmark datasets.3.The results provide insights into the model's effectiveness and potential for real-world application.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retinal vasculature. Given the labor-intensive and costly nature of manual DR diagnosis, along with its low accuracy, developing a DR classification model based on FFA using deep learning techniques is crucial. Furthermore, DR classification faces challenges such as minimal lesion variance between different disease stages and significant size variations of lesions within the same stage, with small lesions often overlooked by existing models. We propose a deep learning model, D-GET, utilizing a Group-Enhanced Transformer for classifying DR lesion severity in FFA images. The D-GET model incorporates a Full-Scale Transformer Block, where the Group-Focal module captures feature information at multiple scales, from fine details to broader patterns, and adaptively integrates contextual information, enhancing the model's ability to detect small-scale lesions. The model also includes a Channel Adaptive Attention Module (CAAM) that synthesizes channel and spatial information to improve feature detection and localization. Experimental findings indicate that the D-GET method we developed surpasses existing methods on a custom dataset. The D-GET model, developed for DR classification using FFA images, significantly improves the detection of small-scale lesions. This advancement enhances the diagnosis and treatment of DR, establishing a solid foundation for its broader application across various domains of ophthalmic and general medical imaging.