Local Structure Awareness-Based Retinal Microaneurysm Detection with Multi-Feature Combination

A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression

As an alternative to assessments performed by human experts, artificial intelligence (AI) is currently being used for screening fundus images and monitoring diabetic retinopathy (DR). Although AI models can provide quasi-clinician diagnoses, they rarely offer new insights to assist clinicians in predicting disease prognosis and treatment response. Using longitudinal retinal imaging data, we developed and validated a predictive model for DR progression: AI-driven Diabetic Retinopathy Progression Prediction Algorithm (ADRPPA). In this retrospective study, we analyzed paired retinal fundus images of the same eye captured at ≥ 1-year intervals. The analysis was performed using the EyePACS dataset. By analyzing 12,768 images from 6384 eyes (2 images/eye, taken 733 ± 353 days apart), each annotated with DR severity grades, we trained the neural network ResNeXt to automatically determine DR severity. EyePACS data corresponding to 5108 (80%), 639 (10%), and 637 (10%) eyes were used for model training, validation, and testing, respectively. We further used an independent e-ophtha dataset comprising 148 images annotated with microaneurysms, 118 (75%) and 30 (25%) of which were used for training and validation, respectively. This dataset was used to train the neural network Mask Region-based Convolutional Neural Network (Mask-RCNN) for quantifying microaneurysms. The DR and microaneurysm scores from the first nonreferable DR (NRDR) image of each eye were used to predict progression to referable DR (RDR) in the second image. The area under the receiver operating characteristic curve values indicating our model's performance in diagnosing RDR were 0.963, 0.970, 0.968, and 0.971 for the trained ResNeXt models with input image resolutions of 256 × 256, 512 × 512, 768 × 768, and 1024 × 1024 pixels, respectively. In the validation of the Mask-RCNN model trained on the e-ophtha dataset resized to 1600 pixels in height, the recall, precision, and F1-score values for detecting individual microaneurysms were 0.786, 0.615, and 0.690, respectively. The best model combination for predicting NRDR-to-RDR progression included the 768-pixel ResNeXt and 1600-pixel Mask-RCNN models; this combination achieved recall, precision, and F1-scores of 0.338 (95% confidence interval [CI]: 0.228-0.451), 0.561 (95% CI: 0.405-0.714), and 0.422 (95% CI: 0.299-0.532), respectively. Thus, deep learning models can be trained on longitudinal retinal imaging data to predict NRDR-to-RDR progression. Furthermore, DR and microaneurysm scores generated from low- and high-resolution fundus images, respectively, can help identify patients at a high risk of NRDR, facilitating timely treatment.

Micro aneurysm detection using optimized residual-based temporal attention Convolutional Neural Network with Inception-V3 transfer learning

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non-proliferative diabetic retinopathy depending on deep learning approaches (DRD-CNN-NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN-AMD-MAFP-Net) respectively. RESEARCH HIGHLIGHTS: Micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT-CNN algorithm successfully identifies the micro aneurysms using EOA.

GravityNet for end-to-end small lesion detection

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

Diabetic retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina, and it has been used in recent years to diagnose many eye diseases. As a result, this paper attempts to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly fluorescein angiography (FA) and OCT images were registered. Then, the MA and normal areas were separated, and the features of each of these areas were extracted using the Bag of Features (BOF) approach with the Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAs automatically is a new idea, and the results obtained as preliminary research in this field are promising.