Deep learning for intracranial hemorrhage detection and classification in brain CT scans: a systematic review and hybrid model approach - Takeaways - MDSpire

Deep learning for intracranial hemorrhage detection and classification in brain CT scans: a systematic review and hybrid model approach

  • By

  • Harshith V.

  • Bhargavram Athray

  • Likhitha T. Murthy

  • Samyukta Joshi

  • Ameena Amreen Ayoob

  • Sai Chakith M. R.

  • Pruthvish Reddy

  • Ranjith Raj

  • Vikram Patil

  • Deepak Benny

  • Shiva Prasad Kollur

  • Kasim Sakran Abass

  • Victor Stupin

  • Sushma Pradeep

  • Chandan Shivamallu

  • Ekaterina Silina

  • April 7, 2026

  • 0 min

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  • 1

    Intracranial hemorrhage (ICH) is a critical emergency requiring prompt diagnosis, with non-contrast CT being the primary imaging method.

  • 2

    Machine learning and deep learning techniques are increasingly utilized for automated detection and classification of ICH and its subtypes.

  • 3

    Hybrid and transformer-based models demonstrate improved performance in feature representation for ICH detection from CT scans.

  • 4

    Challenges such as dataset heterogeneity and generalizability hinder the clinical validation of automated ICH detection models.

  • 5

    Future research should focus on large-scale validation and integration of machine learning models into clinical workflows for neuroimaging.

Original Source(s)

Deep learning for intracranial hemorrhage detection and classification in brain CT scans: a systematic review and hybrid model approach

  • by Harshith V., Bhargavram Athray, Likhitha T. Murthy, Samyukta Joshi, Ameena Amreen Ayoob, Sai Chakith M. R., Pruthvish Reddy, Ranjith Raj, Vikram Patil, Deepak Benny, Shiva Prasad Kollur, Kasim Sakran Abass, Victor Stupin, Sushma Pradeep, Chandan Shivamallu, Ekaterina Silina

  • April 7, 2026

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