An attention based hybrid approach using CNN and BiLSTM for improved skin lesion classification

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

Published: May 5, 2025

Abstract Skin lesions remain a significant global health issue, with their incidence rising steadily over the past few years. Early and accurate detection is crucial for effective treatment improving patient outcomes. This work explores integration of advanced Convolutional Neural Networks (CNNs) Bidirectional Long Short Term Memory (BiLSTM) enhanced by spatial, channel, temporal attention mechanisms to improve classification skin lesions. The hybrid model trained distinguish between various high precision. Among models evaluated, CNN (original architecture) BiLSTM achieved highest performance, an accuracy 92.73%, precision 92.84%, F1 score 92.70%, recall Jaccard Index (JAC) 87.08%, Dice Coefficient (DIC) Matthews Correlation (MCC) 91.55%. proposed was compared other configurations, including Gated Recurrent Units (GRU) mechanisms, LSTM BiGRU BiLSTM, LSTM, BiGRU, GRU, standalone CNN, InceptionV3, Visual Geometry Group-16 (VGG16), Xception, highlight efficacy approach. research aims empower healthcare professionals providing robust diagnostic tool that enhances supports proactive management strategies. model’s ability analyze high-resolution images capture complex features promises advancements in early personalized treatment. not only seeks advance technological capabilities lesion diagnostics but also mitigate disease’s impact through timely interventions improved outcomes, ultimately enhancing public resilience on scale.

Language: Английский

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Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(2)

Published: May 11, 2025

ABSTRACT This overview investigates the evolution and current landscape of eXplainable Artificial Intelligence (XAI) in healthcare, highlighting its implications for researchers, technology developers, policymakers. Following PRISMA protocol, we analyzed 89 publications from January 2000 to June 2024, spanning 19 medical domains, with a focus on Neurology Cancer as most studied areas. Various data types are reviewed, including tabular data, imaging, clinical text, offering comprehensive perspective XAI applications. Key findings identify significant gaps, such limited availability public datasets, suboptimal preprocessing techniques, insufficient feature selection engineering, utilization multiple methods. Additionally, lack standardized evaluation metrics practical obstacles integrating systems into workflows emphasized. We provide actionable recommendations, design explainability‐centric models, application diverse methods, fostering interdisciplinary collaboration. These strategies aim guide researchers building robust AI assist developers creating intuitive user‐friendly tools, inform policymakers establishing effective regulations. Addressing these gaps will promote development transparent, reliable, user‐centred ultimately improving decision‐making patient outcomes.

Language: Английский

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Chi2 weighted ensemble: A multi-layer ensemble approach for skin lesion classification using a novel framework - optimized RegNet synergy with Attention-Triplet

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0321803 - e0321803

Published: May 20, 2025

Skin lesions, including various abnormalities and potentially fatal skin cancers, require early detection for effective treatment. However, current methods often struggle to identify the precise areas responsible these after model dominance dispersion. To address this, we propose a novel Transfer Learning-based framework that integrates Optimized RegNet Synergy architectures Attention-Triplet mechanisms—comprising channel attention, squeeze-excitation soft attention—combined with an advanced Ensemble Learning strategy. A significant gap in research is lack of techniques optimal weight allocation predictions. Our study fills this by introducing Chi2 Weighted (CWE) method, which further enhanced into Multi-Layer id="M2">

Language: Английский

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The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models

Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 26, 2025

Language: Английский

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Skin Cancer Classifier: Performance Enhancement Using Deep Learning Models

2022 8th International Conference on Signal Processing and Communication (ICSC), Journal Year: 2025, Volume and Issue: unknown, P. 721 - 725

Published: Feb. 20, 2025

Language: Английский

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