Explainable AI: A Combined XAI Framework for Explaining Brain Tumour Detection Models

• URL: http://arxiv.org/abs/2602.05240v1
• Date: Thu, 05 Feb 2026 02:58:30 GMT
• Title: Explainable AI: A Combined XAI Framework for Explaining Brain Tumour Detection Models
• Authors: Patrick McGonagle, William Farrelly, Kevin Curran,
• Abstract summary: This study explores the integration of multiple Explainable AI (XAI) techniques to enhance the interpretability of deep learning models for brain tumour detection.<n>A custom Convolutional Neural Network (CNN) was developed and trained on the BraTS 2021 dataset, achieving 91.24% accuracy in distinguishing between tumour and non-tumour regions.
• Score: 0.688204255655161
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study explores the integration of multiple Explainable AI (XAI) techniques to enhance the interpretability of deep learning models for brain tumour detection. A custom Convolutional Neural Network (CNN) was developed and trained on the BraTS 2021 dataset, achieving 91.24% accuracy in distinguishing between tumour and non-tumour regions. This research combines Gradient-weighted Class Activation Mapping (GRAD-CAM), Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) to provide comprehensive insights into the model's decision-making process. This multi-technique approach successfully identified both full and partial tumours, offering layered explanations ranging from broad regions of interest to pixel-level details. GRAD-CAM highlighted important spatial regions, LRP provided detailed pixel-level relevance and SHAP quantified feature contributions. The integrated approach effectively explained model predictions, including cases with partial tumour visibility thus showing superior explanatory power compared to individual XAI methods. This research enhances transparency and trust in AI-driven medical imaging analysis by offering a more comprehensive perspective on the model's reasoning. The study demonstrates the potential of integrated XAI techniques in improving the reliability and interpretability of AI systems in healthcare, particularly for critical tasks like brain tumour detection.

Related papers

• UbiQVision: Quantifying Uncertainty in XAI for Image Recognition [39.47298454012977]
  SHAP explanations can be unstable and unreliable in the presence of epistemic and aleatoric uncertainty.<n>This study uses Dirichlet posterior sampling and Dempster-Shafer theory to quantify the uncertainty that arises from these unstable explanations in medical imaging applications.
  arXiv  Detail & Related papers  (2025-12-23T11:57:34Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Knowledge-Augmented Language Models Interpreting Structured Chest X-Ray Findings [44.99833362998488]
  This paper introduces CXR-TextInter, a novel framework that repurposes powerful text-centric language models for chest X-rays interpretation.<n>We augment this LLM-centric approach with an integrated medical knowledge module to enhance clinical reasoning.<n>Our work validates an alternative paradigm for medical image AI, showcasing the potential of harnessing advanced LLM capabilities.
  arXiv  Detail & Related papers  (2025-05-03T06:18:12Z)
• Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI [2.104687387907779]
  This study presents an integrated approach leveraging the strengths of ensemble learning with hybrid transformer models and convolutional neural networks (CNNs)<n>Our methodology combines robust tumor segmentation capabilities, utilizing axial attention and transformer encoders for enhanced spatial relationship modeling.<n>The results demonstrate outstanding performance, evidenced by quantitative evaluations such as the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95) for segmentation, and Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean-Square Error (MSE) for inpainting.
  arXiv  Detail & Related papers  (2024-12-16T15:10:53Z)
• Analyzing the Effect of $k$-Space Features in MRI Classification Models [0.0]
  We have developed an explainable AI methodology tailored for medical imaging. We employ a Convolutional Neural Network (CNN) that analyzes MRI scans across both image and frequency domains. This approach not only enhances early training efficiency but also deepens our understanding of how additional features impact the model predictions.
  arXiv  Detail & Related papers  (2024-09-20T15:43:26Z)
• Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques [39.97900702763419]
  This study focuses on analyzing dermoscopy images to determine the depth of melanomas. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. Results indicated that the models achieved significant improvements over previous methods.
  arXiv  Detail & Related papers  (2024-06-19T11:07:55Z)
• Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques [37.9243470221619]
  Article explores the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer.<n>Aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications.
  arXiv  Detail & Related papers  (2024-06-01T18:50:03Z)
• Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI [0.0]
  The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer. The methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions.
  arXiv  Detail & Related papers  (2024-04-05T05:00:21Z)
• An Explainable AI Framework for Artificial Intelligence of Medical Things [2.7774194651211217]
  We leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam) The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. We apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis.
  arXiv  Detail & Related papers  (2024-03-07T01:08:41Z)
• Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation [48.08423125835335]
  MT-BI-RADS is a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. It offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy.
  arXiv  Detail & Related papers  (2023-08-27T22:07:42Z)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis [42.624671531003166]
  We introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks.
  arXiv  Detail & Related papers  (2022-08-19T12:06:46Z)
• Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images [0.0]
  We evaluate attribution methods for illuminating how deep neural networks analyze medical images. We attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.
  arXiv  Detail & Related papers  (2022-08-01T16:05:14Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.