Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 96, С. 106501 - 106501

Опубликована: Июнь 1, 2024

Язык: Английский

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Timely detection of skin cancer: An AI-based approach on the basis of the integration of Echo State Network and adapted Seasons Optimization Algorithm

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106324 - 106324

Опубликована: Апрель 22, 2024

Язык: Английский

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A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 21, 2024

Abstract Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin by utilizing Convolution Neural Network architecture optimizing hyperparameters. The proposed aims increase the precision efficacy of recognition consequently enhance patients' experiences. investigation tackle various significant challenges recognition, encompassing feature extraction, model design, utilizes advanced deep-learning methodologies extract complex features patterns from images. We learning procedure deep integrating Standard U-Net Improved MobileNet-V3 with optimization techniques, allowing differentiate malignant benign cancers. Also substituted crossed-entropy loss function Mobilenet-v3 mathematical framework bias accuracy. model's squeeze excitation component was replaced practical channel attention achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been leverage synthetic effectively. dilated convolutions were incorporated into receptive field. hyperparameters utmost importance improving efficiency models. To fine-tune hyperparameter, we employ sophisticated methods such as Bayesian method using pre-trained CNN MobileNet-V3. compared existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 VGG-19 on “HAM-10000 Melanoma Cancer dataset". empirical findings illustrate optimized hybrid outperforms detection segmentation techniques based high 97.84%, sensitivity 96.35%, accuracy 98.86% specificity 97.32%. enhanced performance this research resulted timelier more diagnoses, potentially contributing life-saving outcomes mitigating healthcare expenditures.

Язык: Английский

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Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Journal of Imaging, Год журнала: 2024, Номер 10(4), С. 81 - 81

Опубликована: Март 28, 2024

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or sequence images to recognize content, has been used extensively across industries in recent years. However, the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV potential improve patient monitoring, system efficiencies, while reducing workload. In contrast previous reviews, we focus on end-user CV. First, briefly review categorize other (job enhancement, surveillance automation, augmented reality). We then developments hospital setting, outpatient, community settings. The advances monitoring delirium, pain sedation, deterioration, mechanical ventilation, mobility, surgical applications, quantification workload hospital, for events outside highlighted. To identify opportunities future also completed journey mapping at different levels. Lastly, discuss considerations associated with outline processes algorithm development testing limit expansion healthcare. This comprehensive highlights ideas expanded use

Язык: Английский

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Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Июнь 5, 2024

Abstract Skin cancer is one of the most frequently occurring cancers worldwide, and early detection crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, strict time limits, which can negatively affect diagnostic outcomes. Deep learning–based systems offer quick, accurate testing enhanced research capabilities, providing significant support to dermatologists. In this study, we Swin Transformer architecture by implementing hybrid shifted window-based multi-head self-attention (HSW-MSA) in place conventional (SW-MSA). This adjustment enables model more efficiently process areas skin overlap, capture finer details, manage long-range dependencies, while maintaining memory usage computational efficiency during training. Additionally, study replaces standard multi-layer perceptron (MLP) with a SwiGLU-based MLP, an upgraded version gated linear unit (GLU) module, achieve higher accuracy, faster training speeds, better parameter efficiency. The modified model-base was evaluated using publicly accessible ISIC 2019 dataset eight classes compared against popular convolutional neural networks (CNNs) cutting-edge vision transformer (ViT) models. exhaustive assessment on unseen test dataset, proposed Swin-Base demonstrated exceptional performance, achieving accuracy 89.36%, recall 85.13%, precision 88.22%, F1-score 86.65%, surpassing all previously reported deep learning models documented literature.

Язык: Английский

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An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Symmetry, Год журнала: 2024, Номер 16(3), С. 366 - 366

Опубликована: Март 18, 2024

Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin has higher fatality rate, it expands gradually different areas of the body. The rapid growth automated diagnosis frameworks led combination diverse machine learning, deep computer vision algorithms for detecting clinical samples atypical lesion specimens. Automated methods recognizing that use learning techniques are discussed in this article: convolutional neural networks, and, general, artificial networks. recognition symmetries key point dealing image datasets; hence, developing appropriate architecture as improve performance release capacities network. current study emphasizes need an method identify lesions reduce amount time effort required diagnostic process, well novel aspect using based on analysis concludes underlying research directions future, which will assist better addressing difficulties encountered human recognition. By highlighting drawbacks advantages prior techniques, authors hope establish standard future domain diagnostics.

Язык: Английский

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Early detection of brain tumors: Harnessing the power of GRU networks and hybrid dwarf mongoose optimization algorithm

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 91, С. 106093 - 106093

Опубликована: Фев. 7, 2024

Язык: Английский

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Reliable operation of reconfigurable smart distribution network with real-time pricing-based demand response

Electric Power Systems Research, Год журнала: 2024, Номер 241, С. 111341 - 111341

Опубликована: Дек. 12, 2024

Язык: Английский

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Stochastic economic sizing and placement of renewable integrated energy system with combined hydrogen and power technology in the active distribution network

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 16, 2024

The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in form optimization model, which objective function to reduce annual costs construction maintenance systems. model takes into account operation wind, solar, bio-waste resources, as well hydrogen storage (a combination P2H, H2P, tank), optimal power flow constraints Electrical are administered system. modeling uncertainties regarding quantity load resources achieved through stochastic using Unscented Transformation method. novelties scheme include sizing placement combined power-based system, consideration impacts units, H2P systems network, method calculation time. study's results demonstrate scheme's ability improve technical conditions network by considering In comparison flow, status has been improved approximately 23-45% siting, sizing, management equipment, other words, able losses voltage drop 44.5% 42.4% compared studies. this situation, peak carrying capability increased about 23.7%. addition, case with overvoltage decreased 43.5%. Also, lower time than scenario-based optimization.

Язык: Английский

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Enhancing Stock Portfolio Selection with Trapezoidal Bipolar Fuzzy VIKOR Technique with Boruta-GA Hybrid Optimization Model: A Multicriteria Decision-Making Approach

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 30, 2025

Язык: Английский

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