Cloud-Based Optimized Deep Learning Framework for Automated Glaucoma Detection Using Stationary Wavelet Transform and Improved Grey-Wolf-Optimization with ELM Approach

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104682 - 104682

Опубликована: Апрель 1, 2025

Язык: Английский

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An innovative approach to classify meniscus tears by reducing vision transformers features with elasticnet approach

The Journal of Supercomputing, Год журнала: 2025, Номер 81(4)

Опубликована: Март 11, 2025

Язык: Английский

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Glaucoma diagnosis using Gabor and entropy coded Sine Cosine integration in adaptive partial swarm optimization-based FAWT

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 107, С. 107832 - 107832

Опубликована: Март 26, 2025

Язык: Английский

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PollenMorph AI: quantum contours based segmentation and deep learning for pollen recognition using microscopic images

Iran Journal of Computer Science, Год журнала: 2025, Номер unknown

Опубликована: Апрель 2, 2025

Язык: Английский

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An Intelligent Model of Segmentation and Classification Using Enhanced Optimization‐Based Attentive Mask RCNN and Recurrent MobileNet With LSTM for Multiple Sclerosis Types With Clinical Brain MRI

NMR in Biomedicine, Год журнала: 2025, Номер 38(6)

Опубликована: Апрель 24, 2025

ABSTRACT In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires exceptional amount of skill because abnormalities on scans frequently inconsistent with clinical symptoms, making it difficult to convert the findings into effective treatment strategies. Furthermore, is expensive process, its frequent utilization monitor illness increases costs. To overcome these drawbacks, this research employs advanced technological approaches develop a deep learning system classifying types MS through brain scans. The major innovation model influence convolution network attention concept recurrent‐based disorder; also proposes optimization algorithm tuning parameter enhance performance. Initially, total as 3427 collected from database, in which samples categorized training testing phase. Here, segmentation carried out by adaptive attentive‐based mask regional neural (AA‐MRCNN). phase, MRCNN's parameters finely tuned enhanced pine cone (EPCOA) guarantee outstanding efficiency. Further, segmented image given recurrent MobileNet long short term memory (RM‐LSTM) getting classification outcomes. Through experimental analysis, acquired 95.4% accuracy, 95.3% sensitivity, specificity. Hence, results prove that has high potential appropriately disorder.

Язык: Английский

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ODCS-NSNP: Optic disc and cup segmentation using deep networks enhanced by nonlinear spiking neural P systems

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 108, С. 107935 - 107935

Опубликована: Апрель 28, 2025

Язык: Английский

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Integrating Drone in Agriculture: Addressing Technology, Challenges, Solutions, and Applications to Drive Economic Growth

Remote Sensing Applications Society and Environment, Год журнала: 2025, Номер unknown, С. 101576 - 101576

Опубликована: Май 1, 2025

Язык: Английский

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Pneumonia Screening From Radiology Images Using Homomorphic Transformation Filter‐Based FAWT and Customized VGG‐16

International Journal of Imaging Systems and Technology, Год журнала: 2025, Номер 35(3)

Опубликована: Май 1, 2025

ABSTRACT Pneumonia, attributable to pathogens and autoimmune disorders, accounts for approximately 450 million cases annually. Chest x‐ray analysis remains the gold standard pneumonia detection, DL has revolutionized study of high‐dimensional data, including images, audio, video. This research enhances validates a CAD system distinguishing from normal health states using imaging. paper presents novel methodology that integrates CLHAE Homographic Transformation Filter‐based Flexible Analytical Wavelet Transform (HTF‐FAWT) image decomposition, enabling systematic decomposition pre‐processed input images into four distinct sub‐band across six hierarchical levels. Feature extraction employs VGG‐16 Deep Learning techniques, with extracted features subsequently classified by support vector machine Morlet, Mexican‐hat wavelet, radial basis function kernels. Employing tenfold cross‐validation, our model exhibited remarkable classification performance, achieving an accuracy 97.51%, specificity 97.77%, sensitivity 96.5% in spotting via x‐rays. The utility feature maps Grad‐CAM highlighting critical regions accurate prediction was confirmed, offering visual validation model's efficacy. Statistical examinations validate superior performance proposed framework, demonstrating its potential as expedient diagnostic tool medical imaging specialists rapidly detecting pneumonia. It demonstrates effectiveness various classifiers classification, method outperforming state‐of‐the‐art approaches. diagnosis high (97.51%), visualization, automated interpretation, faster, reliable screening clinical integration reducing reliance on manual assessment radiology.

Язык: Английский

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Comparative Analysis of Convolutional Neural Network and Support Vector Machine for the Prediction of Alzheimer's Disease

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 56 - 66

Опубликована: Янв. 1, 2025

Язык: Английский

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Transfer Learning with ResNet50 for Enhanced Mammographic Breast Cancer Identification

Опубликована: Окт. 4, 2024

Язык: Английский

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