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

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Март 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.

Язык: Английский

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Explainable AI analysis for smog rating prediction

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 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.

Язык: Английский

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

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 3, 2025

Язык: Английский

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SegSurvNet: SE-U-net-based glioma segmentation and overall survival prediction via MHA-NN and stacking regressor

International Journal of Systems Assurance Engineering and Management, Год журнала: 2025, Номер unknown

Опубликована: Июнь 5, 2025

Язык: Английский

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