A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Frontiers in Medicine, Год журнала: 2023, Номер 10

Опубликована: Апрель 5, 2023

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which anywhere from 1 to 15% global population and thus; considered one leading causes chronic (CKD). In addition renal cancer is tenth most prevalent type cancer, accounting for 2.5% all cancers. Artificial intelligence (AI) in medical systems can assist radiologists other healthcare professionals diagnosing different (RD) with high reliability. This study proposes an AI-based transfer learning framework detect RD at early stage. The presented on CT scans images microscopic histopathological examinations will help automatically accurately classify patients using convolutional neural network (CNN), pre-trained models, optimization algorithm images. used CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, NASNetMobile. addition, Sparrow search (SpaSA) enhance model's performance best configuration. Two datasets were used, first dataset four classes: cyst, normal, stone, tumor. case latter, there five categories within second relate severity tumor: Grade 0, 1, 2, 3, 4. DenseNet201 MobileNet four-classes compared others. Besides, SGD Nesterov parameters optimizer recommended by three while two only recommend AdaGrad AdaMax. five-class dataset, Xception best. Experimental results prove superiority proposed over state-of-the-art classification models. records accuracy 99.98% (four classes) 100% (five classes).

Язык: Английский

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Diagnosing Melanomas in Dermoscopy Images Using Deep Learning

Diagnostics, Год журнала: 2023, Номер 13(10), С. 1815 - 1815

Опубликована: Май 22, 2023

When it comes to skin tumors and cancers, melanoma ranks among the most prevalent deadly. With advancement of deep learning computer vision, is now possible quickly accurately determine whether or not a patient has malignancy. This significant since prompt identification greatly decreases likelihood fatal outcome. Artificial intelligence potential improve healthcare in many ways, including diagnosis. In nutshell, this research employed an Inception-V3 InceptionResnet-V2 strategy for recognition. The feature extraction layers that were previously frozen fine-tuned after newly added top trained. study used data from HAM10000 dataset, which included unrepresentative sample seven different forms cancer. To fix discrepancy, we utilized augmentation. proposed models outperformed results previous investigation with effectiveness 0.89 0.91 InceptionResnet-V2.

Язык: Английский

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Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System

Diagnostics, Год журнала: 2023, Номер 13(3), С. 486 - 486

Опубликована: Янв. 29, 2023

Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of multimodal, including surgery, chemotherapy, occasionally, radiation therapy. Preoperative chemotherapy used routinely European studies select indications North American trials. The objective this study was to build a novel computer-aided prediction system preoperative response tumors. A total 63 patients (age range: 6 months-14 years) were included study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging extract texture, shape, functionality-based features from tumors before chemotherapy. proposed consists six steps: (i) delineate tumors' images across three contrast phases; (ii) characterize texture using first- second-order textural features; (iii) shape by applying parametric spherical harmonics model, sphericity, elongation; (iv) capture intensity changes phases describe functionality; (v) apply fusion based on extracted (vi) determine final as responsive or non-responsive via tuned support vector machine classifier. achieved an overall accuracy 95.24%, with 95.65% sensitivity 94.12% specificity. Using along integrated led superior results compared other classification models. This integrates markers learning model make early predictions about how will respond can lead personalized management plans

Язык: Английский

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A survey on sparrow search algorithms and their applications

International Journal of Systems Science, Год журнала: 2023, Номер 55(4), С. 814 - 832

Опубликована: Дек. 28, 2023

The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based that has made some significant advances since its introduction in 2020. A detailed overview of the basic SSA and several SSA-based variants presented this paper. To be specific, first, principle introduced including mechanism implementation process. Second, many improved SSAs are reviewed hybrid, chaotic, adaptive, binary multi-objective SSAs. In addition, applications real scenarios such as machine learning areas, energy systems, path planning image processing. Finally, further research directions discussed. This survey paper aims to provide a timely review on latest developments

Язык: Английский

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A novel Deeplabv3+ and vision-based transformer model for segmentation and classification of skin lesions

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 92, С. 106084 - 106084

Опубликована: Фев. 14, 2024

Язык: Английский

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Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

Diagnostics, Год журнала: 2024, Номер 14(4), С. 454 - 454

Опубликована: Фев. 19, 2024

In recent years, there has been growing interest in the use of computer-assisted technology for early detection skin cancer through analysis dermatoscopic images. However, accuracy illustrated behind state-of-the-art approaches depends on several factors, such as quality images and interpretation results by medical experts. This systematic review aims to critically assess efficacy challenges this research field order explain usability limitations highlight potential future lines work scientific clinical community. study, was carried out over 45 contemporary studies extracted from databases Web Science Scopus. Several computer vision techniques related image video processing diagnosis were identified. context, focus process included algorithms employed, result accuracy, validation metrics. Thus, yielded significant advancements using deep learning machine algorithms. Lastly, establishes a foundation research, highlighting contributions opportunities improve effectiveness learning.

Язык: Английский

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A lightweight deep convolutional neural network model for skin cancer image classification

Applied Soft Computing, Год журнала: 2024, Номер 162, С. 111794 - 111794

Опубликована: Май 27, 2024

Язык: Английский

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Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: An optimal approach

Heliyon, Год журнала: 2024, Номер 10(5), С. e26415 - e26415

Опубликована: Фев. 18, 2024

Skin cancer is a prevalent form of that necessitates prompt and precise detection. However, current diagnostic methods for skin are either invasive, time-consuming, or unreliable. Consequently, there demand an innovative efficient approach to diagnose utilizes non-invasive automated techniques. In this study, unique method has been proposed diagnosing by employing Xception neural network optimized using Boosted Dipper Throated Optimization (BDTO) algorithm. The deep learning model capable extracting high-level features from dermoscopy images, while the BDTO algorithm bio-inspired optimization technique can determine optimal parameters weights network. To enhance quality diversity ISIC dataset utilized, widely accepted benchmark system diagnosis, various image preprocessing data augmentation techniques were implemented. By comparing with several contemporary approaches, it demonstrated outperforms others in detecting cancer. achieves average precision 94.936%, accuracy 94.206%, recall 97.092% surpassing performance alternative methods. Additionally, 5-fold ROC curve error have presented validation showcase superiority robustness method.

Язык: Английский

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A model for skin cancer using combination of ensemble learning and deep learning

PLoS ONE, Год журнала: 2024, Номер 19(5), С. e0301275 - e0301275

Опубликована: Май 31, 2024

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as most prevalent type cancer. In United States, an estimated annual incidence approximately 3.5 million people receiving diagnosis skin underscores its widespread prevalence. Furthermore, prognosis for afflicted with advancing stages experiences substantial decline in survival rates. This paper dedicated to aiding healthcare experts distinguishing between benign malignant cases by employing range machine learning deep techniques different feature extractors selectors enhance evaluation metrics. this paper, transfer models are employed extractors, metrics, selection layer designed, which includes diverse such Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, Variance. Among models, DenseNet-201 was selected primary extractor identify features from data. Subsequently, Lasso method applied selection, utilizing approaches MLP, RF, NB. To optimize accuracy precision, ensemble methods were best-performing models. The study provides sensitivity rates 87.72% 92.15%, respectively.

Язык: Английский

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Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

Symmetry, Год журнала: 2023, Номер 15(7), С. 1369 - 1369

Опубликована: Июль 5, 2023

Skin cancer represents one of the most lethal and prevalent types observed in human population. When diagnosed its early stages, melanoma, a form skin cancer, can be effectively treated cured. Machine learning algorithms play crucial role facilitating timely detection aiding accurate diagnosis appropriate treatment patients. However, implementation traditional machine approaches for disease is impeded by privacy regulations, which necessitate centralized processing patient data cloud environments. To overcome challenges associated with privacy, federated emerges as promising solution, enabling development privacy-aware healthcare systems diagnosis. This paper presents comprehensive review that examines obstacles faced conventional explores integration context privacy-conscious prediction systems. It provides discussion on various datasets available performance comparison techniques lesion prediction. The objective to highlight advantages offered potential addressing concerns realm

Язык: Английский

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