A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images

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

Published: Jan. 10, 2025

Brain tumors present a significant global health challenge, and their early detection accurate classification are crucial for effective treatment strategies. This study presents novel approach combining lightweight parallel depthwise separable convolutional neural network (PDSCNN) hybrid ridge regression extreme learning machine (RRELM) accurately classifying four types of brain (glioma, meningioma, no tumor, pituitary) based on MRI images. The proposed enhances the visibility clarity tumor features in images by employing contrast-limited adaptive histogram equalization (CLAHE). A PDSCNN is then employed to extract relevant tumor-specific patterns while minimizing computational complexity. RRELM model proposed, enhancing traditional ELM improved performance. framework compared with various state-of-the-art models terms accuracy, parameters, layer sizes. achieved remarkable average precision, recall, accuracy values 99.35%, 99.30%, 99.22%, respectively, through five-fold cross-validation. PDSCNN-RRELM outperformed pseudoinverse (PELM) exhibited superior introduction led enhancements performance parameters sizes those models. Additionally, interpretability was demonstrated using Shapley Additive Explanations (SHAP), providing insights into decision-making process increasing confidence real-world diagnosis.

Language: Английский

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Improving Malaria diagnosis through interpretable customized CNNs architectures

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

Published: Feb. 22, 2025

Abstract Malaria, which is spread via female Anopheles mosquitoes and brought on by the Plasmodium parasite, persists as a serious illness, especially in areas with high mosquito density. Traditional detection techniques, like examining blood samples microscope, tend to be labor-intensive, unreliable necessitate specialized individuals. To address these challenges, we employed several customized convolutional neural networks (CNNs), including Parallel network (PCNN), Soft Attention Convolutional Neural Networks (SPCNN), after Functional Block (SFPCNN), improve effectiveness of malaria diagnosis. Among these, SPCNN emerged most successful model, outperforming all other models evaluation metrics. The achieved precision 99.38 $$\pm$$ 0.21%, recall 99.37 F1 score accuracy ± 0.30%, an area under receiver operating characteristic curve (AUC) 99.95 0.01%, demonstrating its robustness detecting parasites. Furthermore, various transfer learning (TL) algorithms, VGG16, ResNet152, MobileNetV3Small, EfficientNetB6, EfficientNetB7, DenseNet201, Vision Transformer (ViT), Data-efficient Image (DeiT), ImageIntern, Swin (versions v1 v2). proposed model surpassed TL methods every measure. 2.207 million parameters size 26 MB, more complex than PCNN but simpler SFPCNN. Despite this, exhibited fastest testing times (0.00252 s), making it computationally efficient both We assessed interpretability using feature activation maps, Gradient-weighted Class Activation Mapping (Grad-CAM) SHapley Additive exPlanations (SHAP) visualizations for three architectures, illustrating why outperformed others. findings from our experiments show significant improvement parasite approach outperforms traditional manual microscopy terms speed. This study highlights importance utilizing cutting-edge technologies develop robust effective diagnostic tools prevention.

Language: Английский

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An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

J — Multidisciplinary Scientific Journal, Journal Year: 2024, Volume and Issue: 7(1), P. 48 - 71

Published: Jan. 22, 2024

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity recent years shown promising results automated medical image analysis, particularly field chest radiology. This paper presents novel DL framework specifically designed for multi-class diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, COVID-19 using images, aiming address need efficient accessible diagnostic tools. The employs convolutional neural network (CNN) architecture with custom blocks enhance feature maps learn discriminative features from images. proposed is evaluated on large-scale dataset, demonstrating superior performance lung. In order evaluate effectiveness presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, specificity improvements. findings study showcased remarkable achieving 98.88%. metrics precision, recall, F1-score, Area Under Curve (AUC) averaged 0.9870, 0.9904, 0.9887, 0.9939 across six-class categorization system. research contributes provides foundation future advancements DL-based systems diseases.

Language: Английский

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Comparative Analysis of Kolmogorov-Inspired CNN and Traditional CNN Models for Pneumonia Detection: A Study on Chest CT Images

International Journal of Statistics in Medical Research, Journal Year: 2025, Volume and Issue: 14, P. 38 - 44

Published: Feb. 5, 2025

Aim: In this study, our goal is to compare the effectiveness of Kolmogorov Inspired Convolutional Neural Networks (KAN) with traditional (CNN) models in pneumonia detection and contribute development more efficient accurate diagnostic tools field medical imaging. Methods: Both are structured same layers hyperparameters ensure a fair comparison their performance. For robust evaluation, relevant dataset was divided into 80% for training 20% testing. Results Conclusion: Performance metrics KAN; 95.2% sensitivity, 97.6% specificity, 94.1% precision, 96.9% accuracy (Acc), 0.9466 F1 score (F1) 0. 9251 Matthews Correlation Coefficient (MCC), while CNN model found 92.5%, 96.4%, 91.2%, 95.3%, 0.9188 0.8858 criteria, indicating that KAN outperformed. This emphasizes has potential be effective chest CT images.

Language: Английский

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HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification

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

Published: Feb. 18, 2025

Language: Английский

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Innovative deep learning solutions for Turkish butterfly species identification: a VGGNet enhancement study

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(5)

Published: March 10, 2025

Language: Английский

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Medivision: Empowering Colorectal Cancer Diagnosis and Tumor Localization Through Supervised Learning Classifications and Grad-CAM Visualization of Medical Colonoscopy Images

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(2)

Published: March 21, 2025

Language: Английский

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Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis

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

Published: March 22, 2025

Abstract Accurate brain tumor segmentation is critical for clinical diagnosis and treatment planning. This study proposes an advanced framework that combines Multiscale Attention U-Net with the EfficientNetB4 encoder to enhance performance. Unlike conventional U-Net-based architectures, proposed model leverages EfficientNetB4’s compound scaling optimize feature extraction at multiple resolutions while maintaining low computational overhead. Additionally, Multi-Scale Mechanism (utilizing $$1\times 1, 3\times 3$$ , $$5\times 5$$ kernels) enhances representation by capturing boundaries across different scales, addressing limitations of existing CNN-based methods. Our approach effectively suppresses irrelevant regions localization through attention-enhanced skip connections residual attention blocks. Extensive experiments were conducted on publicly available Figshare dataset, comparing EfficientNet variants determine optimal architecture. demonstrated superior performance, achieving Accuracy 99.79%, MCR 0.21%, Dice Coefficient 0.9339, Intersection over Union (IoU) 0.8795, outperforming other in accuracy efficiency. The training process was analyzed using key metrics, including Coefficient, dice loss, precision, recall, specificity, IoU, showing stable convergence generalization. method evaluated against state-of-the-art approaches, surpassing them all accuracy, mean IoU. demonstrates effectiveness robust efficient tumors, positioning it as a valuable tool research applications.

Language: Английский

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Enhancing Clinical Documentation Through NLP-Driven Disease Categorization and Visualization: A Case Study Utilizing the Llama2 AI Model

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 193 - 209

Published: Jan. 1, 2025

Language: Английский

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Interpretable AI for Liver Cancer Detection: Cascaded CNN & GLCM Integration

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(2), P. 21243 - 21248

Published: April 3, 2025

Liver cancer has significantly high mortality, especially in regions such as Africa and Asia. Early detection enhances treatment options, but indications are frequently not apparent until advanced stages. This research introduces an explainable AI (XAI) approach using a cascaded Convolutional Neural Network (CNN) combined with Gray Level Co-occurrence Matrix (GLCM)-based texture features to segregate non-cancerous from malicious tumors. The CLD system was used for assessment, the examined TCIA dataset, demonstrating higher accuracy interpretability compared prevailing techniques. XAI methods, feature importance model visualization, were employed provide details on decision-making process of model, ensuring transparency reliability clinical applications.

Language: Английский

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