A new hybrid learning model for early diagnosis of hypertension using IoMT technologies

Ain Shams Engineering Journal, Год журнала: 2025, Номер 16(8), С. 103490 - 103490

Опубликована: Май 23, 2025

Язык: Английский

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An improved EnlightenGAN shadow removal framework for images of cracked concrete

Mechanical Systems and Signal Processing, Год журнала: 2024, Номер 223, С. 111943 - 111943

Опубликована: Сен. 14, 2024

Язык: Английский

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The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

Proceedings of the International Conference on Advanced Technologies, Год журнала: 2023, Номер unknown

Опубликована: Авг. 19, 2023

Early detection of plant diseases in the agricultural sector is considered an important goal to increase productivity and minimize damage. This study deals with use deep learning methods realize automatic leaf peanut plants explicability model heatmap visualizations formed during diseases. In study, a dataset containing 3058 images 5 classes enriched diseased healthy samples leaves was used. The explainability property has also been studied understand why models detect particular disease. decision processes models, which are usually described as "magic box", were visualized method this study. By highlighting pixels that effective detecting visualization, decision-making process tried be made understandable. results show have high performance diseases, obtained by visualization reliable tool for specialists producers. Thanks visual explanations provided model, level confidence increased provided. constitutes step towards increasing efficiency applications providing more efficient approach disease management investigating impact field plants.

Язык: Английский

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PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Neural Computing and Applications, Год журнала: 2023, Номер 36(9), С. 5031 - 5057

Опубликована: Дек. 24, 2023

Abstract Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop spread of cancer, early polyp detection essential. The timely removal polyps without submitting sample for histology made possible computer-assisted classification. In addition to Locally Shared Features (LSF) ensemble learning majority voting, this paper introduces computer-aided decision support system named PolyDSS assist endoscopists segmenting classifying various classes using deep models like ResUNet ResUNet++ transfer EfficientNet. PICCOLO dataset used train test model. address issue class imbalance, data augmentation techniques were on dataset. investigate impact each technique model, extensive experiments conducted. While classification module achieved highest accuracy 0.9425 utilizing strength proposed Dice Similarity Coefficient (DSC) 0.9244 LSF. conjunction with Paris system, its significant results, clinicians identifying choosing best approach treatment.

Язык: Английский

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Dump truck activity recognition using vibration signal and convolutional neural network

Automation in Construction, Год журнала: 2024, Номер 165, С. 105495 - 105495

Опубликована: Июнь 8, 2024

Язык: Английский

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Cracks segmentation of engineering structures in complex backgrounds using a concatenation of Transformer and CNN models driven by scene understanding information

Structures, Год журнала: 2024, Номер 65, С. 106685 - 106685

Опубликована: Июнь 14, 2024

Язык: Английский

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An overview of current developments and methods for identifying diabetic foot ulcers: A survey

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(6)

Опубликована: Окт. 9, 2024

Abstract Diabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays crucial role automated monitoring diagnosis systems, where integration of medical imaging, computer vision, statistical analysis, gait information is essential comprehensive understanding effective management. Diagnosing imperative, as it major processes diagnosis, treatment planning, neuropathy research within systems. To address this, various machine learning deep learning‐based methodologies have emerged literature to support practitioners achieving improved diagnostic analyses DFU. This survey paper investigates DFU, spanning traditional approaches cutting‐edge techniques. It systematically reviews key stages involved diabetic ulcer (DFUC) methods, including preprocessing, feature extraction, classification, explaining their benefits drawbacks. The investigation extends exploring state‐of‐the‐art convolutional neural network models tailored DFUC, involving extensive experiments with data augmentation transfer methods. overview also outlines datasets commonly employed evaluating DFUC methodologies. Recognizing that reduced blood flow lower limbs might be caused by atherosclerotic vessels, this provides recommendations researchers routine therapy prevent complications. Apart from reviewing prior literature, aims influence future diagnostics outlining prospective directions, particularly domains personalized intelligent healthcare. Finally, contribute continual evolution order provide more customized care. article categorized under: Application Areas > Health Care Technologies Machine Learning Artificial Intelligence

Язык: Английский

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State of the art of the automatized characterization of chronic wound patterns

Опубликована: Окт. 15, 2024

Язык: Английский

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Pigmented skin disease classification via deep learning with an attention mechanism

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112571 - 112571

Опубликована: Дек. 1, 2024

Язык: Английский

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HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 28(2), С. 941 - 951

Опубликована: Ноя. 10, 2023

The early lesions of Hashimoto's thyroiditis are inconspicuous, and the ultrasonic features these indistinguishable from other thyroid diseases. This paper proposes a Hashimoto Thyroiditis ultrasound image classification model HT-RCM which consists Residual Full Convolution Transformer (Res-FCT) Channel Attention Module (Res-CAM). To collect low-order information caused by hypoechoic signals accurately, residual connection is injected between FCTs to form Res-FCT helps superimpose input high-order output together. can make focus more on while avoiding gradient dispersion. initial feature map inserted into again through down-sampling component, further exact multi-level original semantic in image. Res-CAM constructed implementing channel attention module convolution layer. effectively increase weights lesion channels suppressing noise channels, makes regions. experimental results our collected dataset show that outperforms mainstream models obtains state-of-the-art performance HT classification.

Язык: Английский

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