<scp>Deep</scp> long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification

Priya, R. Pugal; Sivarani, T. S.; Saravanan, Akila

doi:10.1002/cnm.3560

Cited by 19 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This ensemble model demonstrated better results, with accuracy of 87% in identifying DR stages. Priya et al [148] proposed a deep long short-term memory (LSTM) in a neural network with Red Fox optimization (deep LSTM-RFO) algorithm for classifying normal, mild, moderate, and severe NPDR stages. The proposed method was tested on three datasets such as MESSIDOR, STARE, and DRIVE.…”

Section: B Multi-class Classification For Gradingmentioning

confidence: 99%

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Zhu,

Xiong,

Zhong

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and blindness. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to further analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed.

show abstract

Section: B Multi-class Classification For Gradingmentioning

confidence: 99%

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Zhu,

Xiong,

Zhong

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The study can help to further increase the accuracy of natural language processing systems by optimizing the network parameters with the Fox algorithm and using the LSTM technique to forecast essay results. For tasks like automated grading, sentiment analysis, and information retrieval, the suggested approach may impact daily life [23].…”

Section: Related Workmentioning

confidence: 99%

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Chassab,

Zakaria,

Tiun

2024

IEEE Access

View full text Add to dashboard Cite

The computer-based Automated Essay Scoring (AES) system automatically marks or scores student replies by considering relevant criteria. The methodology, which systematically categorizes writing quality, can increase operational effectiveness in academic and major commercial institutions. To study the projected score, AES relies on extracting numerous aspects from the student's response, including grammatical and textural information. However, the recovered features may result in dimensionality reduction and a challenging-to-understand feature selection procedure. As the number of parameters rises, the model also demands a large cost for processing and training the data. However, these problems worsen the accuracy of score prediction as a whole and widen the gap between actual and anticipated results. This study suggested the Fox-optimized Long Short-Term Memory-based Augmented Language Model (FLSTM-ALM) as a solution to these problems for giving successful training to text features; the model uses an augmented learning paradigm. The retrieval score was then analyzed and generated using a neural knowledge encoder and retriever. The neural model successfully classifies the output based on this score. The best features are then chosen using the fox optimization algorithm based on the food-searching category. This choice of parameters solves the exploration and optimization issue with document classification. The performance of the optimized AES system was assessed using the two datasets, ASAP and ETS, and it demonstrated a high accuracy of 98.92% and a low error rate of 0.096%. Dimensionality reduction can thus be fixed by optimizing the FLSTM-ALM model with an appropriate meta-heuristic method, such as the FOX algorithm, which raises the predicted accuracy, recall, and f1 score for the AES model.

show abstract

“…RFO was integrated with deep long-and short-term memory (LSTM) in a neural network (deep LSTM-RFO) technique for DR classification. Priya et al [43] proposed integrating and employing RFO to improve the performance of deep LSTM during classification. Polap et al [44] developed a federated learning hybrid combining artificial intelligence training and a meta-heuristic, with the Red Fox Optimization algorithm acting as a representation of the meta-heuristic.…”

Section: Related Workmentioning

confidence: 99%

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Ketafa,

Al-Darraji

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper, present a thre Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper presents a three-stage multi-objective path-planning method. The first stage is to locate the best or near-best solution path and avoid detected obstacles using a hybrid of the red fox–gray wolf optimizer (RFO–GWO), which finds a route from the start position to the target position. In the second step, a mutation operation using an evolutionary algorithm is utilized to enhance the length, integrity, and smoothness of the route generated by the RFO–GWO algorithm. The final step of the suggested method is refined further using a multiphase technique. By integrating the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path-planning method resembles the actual world. The simulation results indicate that this strategy creates the most viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when compared to prior path-planning methods, the simulation’s outcomes indicate that the suggested RFO–GWO method is effective in terms of the route, and the strategy is extremely competitive. The results showed a significant improvement, where the total percentage convergence time (in seconds) for RFO–GWO for the three maps was 15%, 12%, and 10%, respectively, whereas it was 35%, 41%, and 43% seconds in GWO and 34%, 35%, and 37% seconds in RFO. There was also a significant improvement in the number of nodes for RFO-GWO (2%, 3%, and 2%) compared to GWO nodes (64%, 65%, and 62%), and RFO nodes (32%, 30%, and 35%) for the same three maps. Subsequently, the smoothness of the path formed by the recommended approach was enhanced using the evolutionary algorithm (EA), where the total percentage length of the path in the worst scenario for GWO was 28% and for RFO was 26% in units, but after improvement with the RFO-GWO with EA, it became 22% in units. stages multi-objective path planning method: The first stage is to locate the best or near-best solution path and avoid the detected obstacles using a hybrid of the Red Fox-Grey Wolf optimizer (RFO-GWO) method, which finds a rout from the start position to the target position. In the second step, a mutation operation by evolutionary, are utilized to enhance the length, integrity, and smooth of the rout generated by the RFO-GWO method. the final step the suggested method is refined further by using the multiphase technique. By integrating both the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path planning method resembles the actual world. The simulation results indicate that this strategy creates the best viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when contrasted with prior path-planning methods, simulation outcomes indicate that the suggested RFO-GWO in terms of route effectivity, the strategy is extremely competitive.

show abstract

Deep long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification

Cited by 19 publications

References 37 publications

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Contact Info

Product

Resources

About