ArticleSensors (Basel, Switzerland)2023
Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enhancing brain tumor detection through deep learning and explainable AI techniques.Scientific reports · 2026Article
- Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images.Discover oncology · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- SMBO: a Sinusoidal Modulation Bi-population Optimizer for brain cancer MRI image clustering.Frontiers in neuroscience · 2026Article
- Fine-tuned ResNet34 for efficient brain tumor classification.Scientific reports · 2025Article
- Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand.Acute and critical care · 2025Article
- Explainable CNN for brain tumor detection and classification through XAI based key features identification.Brain informatics · 2025Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- XAI-BT-EdgeNet: explainable edge-aware deep learning with squeeze-and-excitation for brain tumor detection and prediction.Frontiers in artificial intelligence · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.
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Registered trials
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