Assistive System for the Blind with Voice Output Based on Optical Character Recognition

Dhinakaran, D.; Selvaraj, D.; Sankar, S.; Pavithra, S.; Boomika, R.

doi:10.1007/978-981-19-3679-1_1

Cited by 23 publications

(9 citation statements)

References 7 publications

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“…The 33,136 images for training have been categorized into five stages of diabetic retinopathy. Acquisition of the images was made with many fundus cameras as well as diverse fields of view [17]. We selected a subset of 3549 retinal images from the Kaggle dataset.…”

Section: Proposed Methodologymentioning

confidence: 99%

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Banupriya¹,

Anusuya²

2023

Computers, Materials &Amp; Continua

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In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks (CNN) is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model (EDLM) for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent (SGD) optimizer. The EDLM's effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50. Experimental results show that the EDLM achieves higher average sensitivity by 8.28% for VGG16, by 7.03% for VGG19, by 5.58% for ResNet18, by 4.26% for ResNet 34, and by 2.04% for ResNet 50, respectively.

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Section: Proposed Methodologymentioning

confidence: 99%

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Banupriya¹,

Anusuya²

2023

Computers, Materials &Amp; Continua

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“…The test involved training and evaluating each architecture using the same dataset of liver CT scan images. We measured various performance metrics, including accuracy, precision, recall, and F-measure, to assess their effectiveness in predicting liver tumors [20][21][22][23][24][25]. The results of the ablation test demonstrated that ver3, with the inclusion of the attention mechanism, achieved the highest accuracy of 97.99%.…”

Section: Proposed Methodologiesmentioning

confidence: 99%

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

Kalaiselvi,

Anusuya

2023

CMC

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In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The anticipated model is assessed on a Computed Tomography (CT) scan dataset containing both benign and malignant liver tumors. The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. Additionally, advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors. The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction. It can assist radiologists in their diagnosis and treatment planning. The proposed system achieved a high accuracy of 95.5% in predicting liver tumors, outperforming other state-of-the-art methods.

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“…Since they are made up of mobile nodes that communicate across wireless links without centralized supervision, ad hoc networks are vital to the development of wireless mobile networks [27][28][29][30]. Ad-hoc wireless networks immediately inherit the issues with standard wireless and mobile networking, such as maximizing bandwidth, improving transmission quality, and controlling power [31].…”

Section: Literature Surveymentioning

confidence: 99%

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

Dhinakaran,

Sankar,

Elumalai

et al. 2023

IJRITCC

Self Cite

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A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.

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Assistive System for the Blind with Voice Output Based on Optical Character Recognition

Cited by 23 publications

References 7 publications

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

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