CNN-TumorNet: leveraging explainability in deep learning for precise brain tumor diagnosis on MRI images

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: March 26, 2025

Introduction The early identification of brain tumors is essential for optimal treatment and patient prognosis. Advancements in MRI technology have markedly enhanced tumor detection yet necessitate accurate classification appropriate therapeutic approaches. This underscores the necessity sophisticated diagnostic instruments that are precise comprehensible to healthcare practitioners. Methods Our research presents CNN-TumorNet, a convolutional neural network categorizing images into non-tumor categories. Although deep learning models exhibit great accuracy, their complexity frequently restricts clinical application due inadequate interpretability. To address this, we employed LIME technique, augmenting model transparency offering explicit insights its decision-making process. Results CNN-TumorNet attained 99% accuracy rate differentiating from scans, underscoring reliability efficacy as instrument. Incorporating guarantees model’s judgments comprehensible, enhancing adoption. Discussion Despite overarching challenge interpretability persists. These may function ”black boxes,” complicating doctors’ ability trust accept them without comprehending rationale. By integrating LIME, achieves elevated alongside transparency, facilitating environments improving care neuro-oncology.

Language: Английский

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A Critical Review on Segmentation of Glioma Brain Tumor and Prediction of Overall Survival

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 17, 2024

Language: Английский

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Predicting hepatocellular carcinoma survival with artificial intelligence

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

Published: Feb. 20, 2025

Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, survival outcomes have remained unsatisfactory. The aim of this was to evaluate ability machine learning (ML) methods in predicting probability HCC patients. study retrospectively analyzed cases patients with stage 1-4 HCC. Demographic, clinical, pathological, and laboratory data served as input variables. researchers employed feature selection techniques identify key predictors patient mortality. Additionally, utilized a range model rates. included 393 individuals For early-stage (stages 1-2), models reached recall values ​​of up 91% for 6-month prediction. advanced-stage (stage 4), achieved accuracy 92% 3-year overall To predict whether are ex or not, 87.5% when using all 28 features without best performance coming from implementation weighted KNN. Further improvements accuracy, reaching 87.8%, were by applying medium Gaussian SVM. This demonstrates that can reliably probabilities across disease stages. also shows AI accurately high proportion surviving assessing clinical pathological factors.

Language: Английский

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Explainable AI analysis for smog rating prediction

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

Published: March 8, 2025

Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, ammonia from fertilizers, vehicles play significant role. Individually, vehicle's contribution may be small, but collectively, vast number of has substantial impact. Manually assessing each vehicle impractical. However, advancements in machine learning make it possible quantify contribution. By creating dataset with features such as model, year, fuel consumption (city), type, predictive model can classify based on their impact, rating them scale 1 (poor) 8 (excellent). This study proposes novel approach using Random Forest Explainable Boosting Classifier models, along SMOTE (Synthetic Minority Oversampling Technique), predict individual vehicles. The results outperform previous studies, proposed achieving an accuracy 86%. Key performance metrics Mean Squared Error 0.2269, R-Squared (R2) 0.9624, Absolute 0.2104, Explained Variance Score 0.9625, Max 4.3500. These incorporate explainable AI techniques, both agnostic specific provide clear actionable insights. work represents step forward, was last updated only five months ago, underscoring timeliness relevance research.

Language: Английский

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MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 20, 2025

Language: Английский

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Brain tumor detection with bi-directional cascade Gaussian kernel feature-generative adversarial networks

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107838 - 107838

Published: March 26, 2025

Language: Английский

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Addressing cross-population domain shift in chest X-ray classification through supervised adversarial domain adaptation

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

Published: April 3, 2025

Language: Английский

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FGA-Net: Feature-Gated Attention for Glioma Brain Tumor Segmentation in Volumetric MRI Images

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 66 - 87

Published: Dec. 26, 2024

Language: Английский

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Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Language: Английский

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