Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI

• URL: http://arxiv.org/abs/2507.09630v1
• Date: Sun, 13 Jul 2025 13:50:50 GMT
• Title: Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI
• Authors: Shomukh Qari, Maha A. Thafar,
• Abstract summary: This study proposed an artificial intelligence framework for multiclass stroke classification using CT scan images.<n>The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification.<n>To enhance model generalization and address class imbalance, we applied data augmentation techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stroke is one of the leading causes of death globally, making early and accurate diagnosis essential for improving patient outcomes, particularly in emergency settings where timely intervention is critical. CT scans are the key imaging modality because of their speed, accessibility, and cost-effectiveness. This study proposed an artificial intelligence framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from a dataset provided by the Republic of Turkey's Ministry of Health. The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification, with additional transformer variants (vision transformer, transformer-in-transformer, and ConvNext). To enhance model generalization and address class imbalance, we applied data augmentation techniques, including synthetic image generation. The MaxViT model trained with augmentation achieved the best performance, reaching an accuracy and F1-score of 98.00%, outperforming all other evaluated models and the baseline methods. The primary goal of this study was to distinguish between stroke types with high accuracy while addressing crucial issues of transparency and trust in artificial intelligence models. To achieve this, Explainable Artificial Intelligence (XAI) was integrated into the framework, particularly Grad-CAM++. It provides visual explanations of the model's decisions by highlighting relevant stroke regions in the CT scans and establishing an accurate, interpretable, and clinically applicable solution for early stroke detection. This research contributed to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and enhancing access to timely and optimal stroke diagnosis in emergency departments, thereby saving more lives.

Related papers

• Glioblastoma Overall Survival Prediction With Vision Transformers [6.318465743962574]
  Glioblastoma is one of the most aggressive and common brain tumors, with a median survival of 10-15 months.<n>In this study, we propose a novel Artificial Intelligence (AI) approach for Overall Survival (OS) prediction using Magnetic Resonance Imaging (MRI) images.<n>We exploit Vision Transformers (ViTs) to extract hidden features directly from MRI images, eliminating the need of tumor segmentation.<n>The proposed model was evaluated on the BRATS dataset, reaching an accuracy of 62.5% on the test set, comparable to the top-performing methods.
  arXiv  Detail & Related papers  (2025-08-04T13:59:57Z)
• An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging [5.3785187022022845]
  Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide.<n>In this study, we propose Retformer, a novel transformer-based architecture for detecting AD using retinal imaging modalities.
  arXiv  Detail & Related papers  (2025-07-06T06:40:42Z)
• An Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography (CT) Images [0.0]
  Brain stroke is a leading cause of mortality and long-term disability worldwide.<n>Most stroke classification techniques use a single slice-level prediction mechanism.<n>This study investigates machine learning models for early brain stroke prediction using CT scan images.
  arXiv  Detail & Related papers  (2025-07-04T13:11:29Z)
• Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models [40.325359811289445]
  This paper uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data.<n>The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes.
  arXiv  Detail & Related papers  (2025-06-06T11:52:07Z)
• Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition [2.1358421658740214]
  This study introduces a data-efficient image transformer (DeIT)-based approach to advance AI-powered medical imaging and disease detection.<n>It achieves state-of-the-art performance, with the highest AUC, F1 score, precision, minimal loss, and Top-5 accuracy.<n>Grad-CAM++ visualizations improve interpretability by highlighting critical pathological regions, enhancing the model's clinical relevance.
  arXiv  Detail & Related papers  (2025-05-11T13:51:56Z)
• GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis [44.99833362998488]
  We present a novel approach that combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis.<n>Our findings illustrate significant advancements in the precision of segmentation and classification.<n>This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies.
  arXiv  Detail & Related papers  (2025-02-23T23:28:47Z)
• Multi-Scale Transformer Architecture for Accurate Medical Image Classification [4.578375402082224]
  This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture.<n>By integrating a multi-scale feature fusion mechanism and refining the self-attention process, the model effectively extracts both global and local features.<n>Performance evaluation on the ISIC 2017 dataset demonstrates that the improved Transformer surpasses established AI models.
  arXiv  Detail & Related papers  (2025-02-10T08:22:25Z)
• Object Detection for Medical Image Analysis: Insights from the RT-DETR Model [40.593685087097995]
  This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data.<n>The proposed RT-DETR model, built on a Transformer-based architecture, excels at processing high-dimensional and complex visual data with enhanced robustness and accuracy.
  arXiv  Detail & Related papers  (2025-01-27T20:02:53Z)
• Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting [37.9434503914985]
  A major challenge for machine learning is to control the precision of predictions while enabling a high recall.<n>We use a multiple testing procedure that gives an overperforming way to solve the standard Precision-Recall trade-off.<n>As it is model-agnostic, this contextual selection procedure paves the way to the development of a strategy that can improve the performance of any model without the need of retraining it.
  arXiv  Detail & Related papers  (2025-01-23T19:04:47Z)
• Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin [0.0]
  Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin.
  arXiv  Detail & Related papers  (2024-05-01T23:40:12Z)
• Classification of lung cancer subtypes on CT images with synthetic pathological priors [41.75054301525535]
  Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images. We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
  arXiv  Detail & Related papers  (2023-08-09T02:04:05Z)
• 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)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

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.