Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Jagadesh, B. N.; Karthik, M. Ganesh; Siri, D.; Shareef, S. K. Khaja; Mantena, Srihari Varma; Vatambeti, Ramesh

doi:10.1109/access.2023.3330436

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…The technique stands very special in balancing computational e ciency and performance. Jagadesh et al [19] suggested a framework that integrated the IC2T model for segmentation with Rock Hyrax swarm-based coordination attention mechanism for classi cation. Their approach has been able to depict the potentials of using bio-inspired algorithms in the enhancement of medical image analysis with improved accuracy.…”

Section: Review Of Existing Models Used For Identi Cation Of Diabetic...mentioning

confidence: 99%

ERBVS: Enhanced Retinal Blood Vessel Segmentation using Multiple Modalities and Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations

Thool

2024

Preprint

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It would, therefore, require highly advanced prediction tools to enhance early diagnosis and preemptive mechanisms for all these burgeoning diseases. Fast and correct disease prediction and pre-emption have huge potential for changing clinical outcome and ensuring timely and effective interventions that reduce morbidity and mortality. Current predictive models, instrumental as they are, have been found faltering in precision, recall, accuracy, and timeliness. Such delays and inaccuracies often miss the therapeutic window or lead to misguided clinical decisions. In this work, we present a novel model that aims to quite dramatically improve the process of segmentation and classification. Our approach embeds Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations, together with a multimodal approach, which places it substantially higher across several metrics. This improves the precision, accuracy, recall, and AUC by 8.5%, 8.3%, 4.9%, and 3.9%, respectively, for segmentation and classification, while reducing the classification delay by 5.9% in different situations. Not only does our model handle the intrinsic limitations of current methods, but it also shows flexibility for a wide range of clinical applications. The compelling improvements in classification and preemption metrics strengthen its potential to make a sea change in the disease prediction framework for establishing optimum patient outcomes and efficient scenarios of healthcare delivery.

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Section: Review Of Existing Models Used For Identi Cation Of Diabetic...mentioning

confidence: 99%

ERBVS: Enhanced Retinal Blood Vessel Segmentation using Multiple Modalities and Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations

Thool

2024

Preprint

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“…According to research on breast cancer screening, 20% of women had malignant tumors [7]. Breast tumors may be identified over an extended duration of time by utilizing a number of methods, including X-rays, ultrasound, mammography, magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography [8]. Breast cancer in its early stages may be assessed by screening, which finds the disease when small breast symptoms appear.…”

Section: Introductionmentioning

confidence: 99%

Detection of Breast Cancer in Mammogram Images Using Multi Attention Feature Extraction with Hybrid RSA Based AlexNet

Bommagani,

Challageri,

Naik

et al. 2024

IJCMEM

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Breast tumors have become one of the most frequent illnesses among women, with 287,850 new cases projected to be discovered in 2022. Of those, 43,250 women passed away from this malignancy. The mortality rate for cancer might be decreased through early detection. Despite this, employing mammography photographs to manually identify this kind of cancer is a challenging process that always demands an expert. In the literature, a number of AI-based (Artificial Intelligence) strategies have been proposed. However, they still deal with issues including irrelevant feature extraction, inadequate training models, and similarities between cancerous and non-cancerous areas. In order to identify breast cancer, this research suggested an SMO-MAFNet-Hybrid Alexnet model. The images in this study were first preprocessed to get rid of noise. After that, the multi-attention fusion network (MAFNet) is used to extract features. The Spider Monkey Optimization (SMO) method is utilized in this work to optimize the learning rate in MAFNet. Following feature extraction, classification is done using the AlexNet model. In this work, hybrid optimization, namely Ant Colony Optimization-Reptile Search Algorithm (ACO-RSA), is applied to fine-tune the hyperparameters in AlexNet classification. The suggested method was tested using the CBIS-DDSM (Curated breast imaging subset of Digital Database for Screening Mammography) dataset and demonstrated an accuracy of 98%, outperforming previous models.

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“…Earlier, the majority of techniques used in the field of ocular diagnostics have been somewhat useful but have limitations in terms of precision and adaptability. Conventional image analysis methods have had difficulty reliably identifying the subtle patterns suggestive of diabetic retinopathy [20] since they are frequently built on singlealgorithm frameworks. This discrepancy results from a deficiency of dynamic flexibility to the various retinal alterations that manifest themselves.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Diabetic Retinopathy Diagnosis: A Comparative Analysis of models with an Ensemble classifier and Deep Q Learning

Minakshee Chandankhede

2024

jes

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- A novel method that combines the strengths of different classifiers such as Naive Bayes, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) is introduced in this paper. This method tackles the urgent need for cutting-edge diagnostic methods in the field of ophthalmology, mainly for the identification of diabetic retinopathy (DR). The approach is ensemble-based. Classical methods in retinal analysis of images often fail as they are static and are unable to adjust to the unique details that each distinct image presents. This constraint results in less accurate and precise diagnostic results, highlighting the urgent need for more adaptable and dynamic methods. The suggested model differs significantly from previous methods. Through the use of an ensemble approach, it capitalizes on the distinct advantages of each classifier: the MLP process's sophisticated feature extraction skill, Naive Bayes' probabilistic analysis, and SVM's non-linear pattern recognition capacity. By combining these techniques, the inherent drawbacks of utilizing a single strategy are addressed, guaranteeing a more thorough examination of retinal samples and images. The core of this idea is the system using Deep Q Learning (DQL) for adaptive classifier selection. Using learned Q Values for various contexts, this reinforcement learning technique selects the best classifier adaptively for each unique retinal image, hence optimizing the ensemble. This approach not only advances diagnosis accuracy and precision but also guarantees ongoing learning and adaptation to keep up with changing data patterns and advances in imaging technology. Extensive experiments on the IDRiD & EyePACS Dataset show the effectiveness of this model with a 5.5% increase in overall accuracy with other performance metrics, the results show a significant improvement over the current method. They represent a significant advancement in the timely and accurate identification of diabetic retinopathy, which will ultimately benefit patients and lessen the strain on healthcare systems. Thus, this work represents a major step forward in patient care as well as a technological advance, opening the door to more efficient supervision and treatment of retinal illnesses.

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Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Cited by 4 publications

References 48 publications

ERBVS: Enhanced Retinal Blood Vessel Segmentation using Multiple Modalities and Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations

ERBVS: Enhanced Retinal Blood Vessel Segmentation using Multiple Modalities and Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations

Detection of Breast Cancer in Mammogram Images Using Multi Attention Feature Extraction with Hybrid RSA Based AlexNet

Enhanced Diabetic Retinopathy Diagnosis: A Comparative Analysis of models with an Ensemble classifier and Deep Q Learning

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