Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 168, P. 107723 - 107723

Published: Nov. 19, 2023

Language: Английский

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Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123747 - 123747

Published: March 20, 2024

Language: Английский

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Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 6, 2025

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on modified UNet architecture. To improve accuracy, model integrates attention mechanisms, such as Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, EfficientNet. These mechanisms enable focus more effectively relevant areas, resulting in significant performance improvements. Models incorporating outperformed those without, reflected superior evaluation metrics. The effects of Dice Loss Binary Cross-Entropy (BCE) model's were also analyzed. maximized overlap between predicted actual masks, leading precise boundary delineation, while BCE achieved higher recall, improving detection areas. Grad-CAM visualizations further demonstrated that attention-based models enhanced interpretability by accurately highlighting findings denote combining framework can yield reliable accurate segmentation. Future research will explore use multi-modal imaging, real-time deployment clinical applications, performance.

Language: Английский

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Breast cancer detection using deep learning techniques: challenges and future directions

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Language: Английский

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Computer‐Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI)

Journal of Magnetic Resonance Imaging, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) key tool due its substantial sensitivity invasive breast cancers. Computer‐aided (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas interest, extracting quantitative features, integrating with computer‐aided diagnosis (CADx) pipelines. This review aims provide comprehensive overview current state CADe MRI, technical details pipelines segmentation models including classical intensity‐based methods, supervised unsupervised machine learning (ML) approaches, latest deep (DL) architectures. It highlights recent advancements from traditional algorithms sophisticated DL such as U‐Nets, emphasizing implementation multi‐parametric acquisitions. Despite these advancements, face challenges like variable false‐positive negative rates, complexity interpreting extensive data, variability system performance, lack large‐scale studies multicentric models, limiting generalizability suitability clinical implementation. Technical issues, image artefacts need reproducible explainable algorithms, remain significant hurdles. Future directions emphasize developing more robust generalizable AI improve transparency trust among clinicians, multi‐purpose systems, incorporating large language diagnostic reporting patient management. Additionally, efforts standardize streamline protocols aim increase accessibility reduce costs, optimizing use practice. Level Evidence NA Efficacy Stage 2

Language: Английский

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Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

Bioengineering, Journal Year: 2023, Volume and Issue: 10(4), P. 495 - 495

Published: April 20, 2023

In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems both industry and academia. However, current POI strategies suffer from the lack of sufficient mixing details features related to individual users their corresponding contexts. To overcome this issue, we propose deep learning model based on an attention mechanism study. The suggested technique employs that focuses pattern’s friendship, which is responsible for concentrating relevant users. compute context-aware similarities among diverse users, our six each user as inputs, including ID, hour, month, day, minute, second visiting time, explore influences spatial temporal addition, incorporate geographical information into by creating eccentricity score. Specifically, map trajectory shape, such circle, triangle, or rectangle, different value. This attention-based evaluated two widely used datasets, experimental outcomes prove noteworthy improvement over state-of-the-art recommendation.

Language: Английский

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BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107255 - 107255

Published: July 10, 2023

Language: Английский

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ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

Soft Computing, Journal Year: 2023, Volume and Issue: unknown

Published: July 22, 2023

Language: Английский

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A deep learning model for ergonomics risk assessment and sports and health monitoring in self-occluded images

Signal Image and Video Processing, Journal Year: 2023, Volume and Issue: 18(2), P. 1161 - 1173

Published: Oct. 29, 2023

Language: Английский

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A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 296, P. 108807 - 108807

Published: April 2, 2024

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with extreme gradient boosting (XGBoost) model to propose novel NGO-XGBoost model. performance was evaluated using meteorological data from 30 stations North China Plain and compared XGBoost, random forest (RF), k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method results RF, adaptive (AdaBoost), categorical (CatBoost) models used obtain importance factors estimating ETo, thereby determine optimal combination inputs indicated that by top 3, 4, 5 important as input combinations, all achieved high estimation accuracy. It worth noting there were significant spatial differences precisions four models, but exhibited consistently precisions, global indicator (GPI) rankings 1st, range coefficient determination (R2), nash efficiency (NSE), root mean square error (RMSE), absolute (MAE) bias (MBE) 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 −0.254–0.062 respectively. Furthermore, accuracy varied across different seasons, which more significantly affected humidity wind speed winter. When target station insufficient, trained historical neighboring still maintained precision. Overall, recommends reliable for provides calculating absence data.

Language: Английский

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