Comprehensive machine and deep learning analysis of sensor-based human activity recognition

Neural Computing and Applications, Год журнала: 2023, Номер 35(17), С. 12793 - 12831

Опубликована: Март 8, 2023

Язык: Английский

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Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions

Computers in Biology and Medicine, Год журнала: 2024, Номер 175, С. 108412 - 108412

Опубликована: Апрель 16, 2024

Язык: Английский

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A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

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

Опубликована: Янв. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Язык: Английский

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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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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 Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD)

Bioengineering, Год журнала: 2024, Номер 11(7), С. 711 - 711

Опубликована: Июль 13, 2024

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep and computer vision, fundamentally transforming the analysis retinal images. By utilizing a wide array visual cues extracted from fundus images, sophisticated artificial intelligence models have been developed diagnose various disorders. This paper concentrates on detection Age-Related Macular Degeneration (AMD), significant condition, by offering an exhaustive examination recent learning methodologies. Additionally, it discusses potential obstacles constraints associated with implementing this technology field ophthalmology. Through systematic review, research aims assess efficacy techniques discerning AMD different modalities as they shown promise disorders diagnosis. Organized around prevalent datasets imaging techniques, initially outlines assessment criteria, image preprocessing methodologies, frameworks before conducting thorough investigation diverse approaches for detection. Drawing insights more than 30 selected studies, conclusion underscores current trajectories, major challenges, future prospects diagnosis, providing valuable resource both scholars practitioners domain.

Язык: Английский

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Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

International journal of intelligent engineering and systems, Год журнала: 2024, Номер 17(2), С. 39 - 49

Опубликована: Фев. 28, 2024

The uncontrolled growth of cells in human brain can lead to the formation tumors, which occur all age people.The tumor affect nerve cells, soft tissues and blood vessels.The early detection is necessary aid doctors treating cancer patients increase their survival rate.For this various deep learning models are created discovered for efficient classification.In paper, Convolutional Neural Network proposed classification MRI images using BRATS 2019, 2020 2021 dataset.The min-max normalization used research data preprocessing fed segmentation process.The mask region-based CNN employed segmenting tumors; Followed by that, Batch applied enhance training process minimize overfitting issues.The obtained result shows that model achieves better accuracy 99.55% on 99.80% 99.29% dataset ensures accurate compared with other existing methods like 3D U-Net CapsNet + latent-dynamic condition random field (LDCRF) post-processing.

Язык: Английский

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Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI

Bioengineering, Год журнала: 2024, Номер 11(6), С. 629 - 629

Опубликована: Июнь 19, 2024

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays crucial role in improving patient outcomes. This study introduces non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the of prostate (PCa). IVIM imaging enables differentiation water molecule diffusion within capillaries outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes two-step segmentation through use three U-Net architectures extracting tumor-containing regions interest (ROIs) from segmented images. performance CAD thoroughly evaluated, considering optimal classifier comparing diagnostic value commonly used apparent coefficient (ADC). results demonstrate combination central zone (CZ) peripheral (PZ) features Random Forest Classifier (RFC) yields best performance. achieves an accuracy 84.08% balanced 82.60%. showcases sensitivity (93.24%) reasonable specificity (71.96%), along good precision (81.48%) F1 score (86.96%). These findings highlight effectiveness accurately segmenting diagnosing PCa. represents advancement methods early PCa, showcasing potential machine learning techniques. developed solution has to revolutionize PCa diagnosis, leading improved outcomes reduced healthcare costs.

Язык: Английский

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Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Год журнала: 2024, Номер unknown, С. 1681 - 1686

Опубликована: Июль 2, 2024

Язык: Английский

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A hierarchical sparrow search algorithm to solve numerical optimization and estimate parameters of carbon fiber drawing process

Artificial Intelligence Review, Год журнала: 2023, Номер 56(S1), С. 1113 - 1148

Опубликована: Июль 19, 2023

Язык: Английский

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