{
  "abstract": "Automated classification of brain tumors from magnetic resonance imaging (MRI) can support radiologists by narrowing down differential diagnoses, but clinical usefulness depends on a model being both accurate and interpretable. In this work we design and empirically compare hybrid deep-learning architectures that combine a convolutional neural network (CNN), a Swin-style Transformer sequence refiner, and a bidirectional long short-term memory (BiLSTM) network for four-class brain tumor classification: Glioma, Meningioma, No Tumor, and Pituitary. A full hybrid model (ResNet-50 → Transformer refiner → BiLSTM) trained for fifteen epochs reaches 94.42% test accuracy, a macro F1-score of 94.44%, and a macro one-vs-rest ROC-AUC of 99.63%. To understand which architectural component drives this performance, three two-component variants — CNN+Swin, CNN+LSTM, and a genuine Swin Transformer encoder paired with a BiLSTM (Swin+LSTM) — were trained under an identical three-epoch budget and evaluated on the same held-out test set. Swin+LSTM was consistently the strongest of the three, reaching 90.25% accuracy and a macro F1-score of 90.28%, ahead of CNN+Swin (84.42%) and CNN+LSTM (81.33%). Grad-CAM heatmaps are provided to visualize which regions of an MRI slice drove a given prediction, and the resulting pipeline is deployed through an interactive Streamlit application. The results suggest that pairing a windowed-attention Transformer encoder with a sequence-aggregating recurrent head is a particularly effective and comparatively lightweight combination for this task.",
  "abstract_shown": true,
  "affiliations": [
    "Dept. of Artificial Intelligence and Data Science Priyadarshini College of Engineering Nagpur, India"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Viraj Wankhade"
    },
    {
      "affs": [
        0
      ],
      "name": "Vrushali Paratkar"
    },
    {
      "affs": [
        0
      ],
      "name": "Bhuvan Bhiogade"
    },
    {
      "affs": [
        0
      ],
      "name": "Tushar Baghele"
    },
    {
      "affs": [
        0
      ],
      "name": "Prof. Dr. U. P. Akare Akare"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3hjkn22zzqdy0.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23642879",
  "fulltext_url": null,
  "id": "3hjkn22zzqdy0",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23642879",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3hjkn22zzqdy0.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:21:10Z",
      "native_id": "23642879",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23642879"
    }
  ],
  "subjects": [],
  "title": "Hybrid CNN–Transformer–LSTM Architectures for Explainable Brain Tumor MRI Classification",
  "title_text": "Hybrid CNN–Transformer–LSTM Architectures for Explainable Brain Tumor MRI Classification",
  "type": "preprint",
  "updated_at": "2026-11-08T12:21:10Z",
  "url": "https://research.draconitic.com/paper/3hjkn22zzqdy0",
  "venue": "Hybrid CNN–Transformer–LSTM Architectures for Explainable Brain Tumor MRI Classification",
  "versions": []
}