{
  "abstract": "Brain tumors are a major clinical threat and are associated with high mortality, making accurate and timely diagnosis from magnetic resonance imaging (MRI) essential for treatment planning. Convolutional neural networks (CNNs) have improved automated MRI analysis. However, many existing models remain computationally expensive and do not fully exploit efficient multiscale feature fusion because they rely on predominantly single-branch, sequential feature extraction. Here, we propose HayabusaNet, a compact CNN that uses depthwise separable convolutions to improve efficiency while preserving discriminative capacity. The network employs parallel multiscale branches with varied kernel sizes and progressively increased channel widths to capture fine-grained and coarse tumor patterns. We introduce a tailored multiscale feature-fusion scheme to integrate branch outputs, and we incorporate a hybrid attention module to enhance salient local cues while injecting global contextual dependencies. HayabusaNet is evaluated on two public benchmarks: the multiclass Kaggle Brain Tumor MRI dataset (7,023 images) and the binary Br35H dataset (3,000 images). Model explanations are examined using Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-CAM++. On the Kaggle dataset, HayabusaNet achieves 98.86% accuracy, 98.84% precision, 98.78% recall, and a 98.81% F1-score. On Br35H, it attains 98.70% for accuracy, precision, recall, and F1-score. Compared with other deep CNNs, the proposed model improves computational efficiency through fewer parameters, lower FLOPs, and faster inference, while maintaining strong classification performance. In conclusion, these results show that combining multiscale feature fusion, efficient convolutions, and hybrid attention can improve the accuracy–efficiency balance for brain MRI classification. The framework also supports interpretation through saliency visualizations.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Electrical Engineering and Computer Science, Graduate School of Natural Science and Technology, Kanazawa University, Kakuma Campus, Kanazawa, 920-1192, Japan",
    "Informatics Study Program, School of Computing, Telkom University, Main Campus (Bandung Campus), Jl. Telekomunikasi No. 1, Bandung, 40257, West Java, Indonesia",
    "School of Entrepreneurial and Innovation Studies, College of Transdisciplinary Sciences for Innovation, Kanazawa University, Kakuma Campus, Kanazawa, 920-1192, Japan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rizal Dwi Prayogo",
      "orcid": "0000-0001-7131-8786"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Siti Amatullah Karimah"
    },
    {
      "affs": [
        2
      ],
      "name": "Hidetaka Nambo",
      "orcid": "0000-0003-4223-6145"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b0phpgm8wz5j5.bib",
  "cited_by": 5,
  "doi": "10.1016/j.eswa.2026.133135",
  "fulltext_url": null,
  "id": "b0phpgm8wz5j5",
  "issns": [
    "0957-4174",
    "1873-6793"
  ],
  "landing_url": "https://doi.org/10.1016/j.eswa.2026.133135",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2808|Brain Tumor Detection and Classification"
    ]
  ],
  "openalex": "W7163696734",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-05",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/b0phpgm8wz5j5.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T13:20:36Z",
      "native_id": "W7163696734",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.eswa.2026.133135"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2808|Brain Tumor Detection and Classification",
      "subfield": "neurology"
    }
  ],
  "title": "HayabusaNet: Hybrid attention-based multiscale fusion CNN for accurate and efficient brain tumor classification in MRI scans",
  "title_text": "HayabusaNet: Hybrid attention-based multiscale fusion CNN for accurate and efficient brain tumor classification in MRI scans",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:36Z",
  "url": "https://research.draconitic.com/paper/b0phpgm8wz5j5",
  "venue": "Expert Systems with Applications",
  "versions": []
}