PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification - Top-Institutions - MDSpire

PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification

  • By

  • Mithila Arman

  • Ahnaf Samin

  • A. K. M. Muzahidul Islam

  • Md Maruf Rusafi Arnob

  • Md Jahirul Islam

  • Ishtiak Al Mamoon

  • May 20, 2026

  • 0 min

Share

Top Institutions in Oncology

Brief introduction explaining scope and methodology.

  • #1

    University of Pennsylvania
    University of Pennsylvania

    Philadelphia, Pennsylvania

    Key Differentiators

    • Radiology
    • Neuro-oncology

  • #2

    Mass General Brigham
    Mass General Brigham

    Boston, Massachusetts

    Key Differentiators

    • Radiology
    • Neuro-oncology

  • #3

    Seoul National University Hospital
    Seoul National University Hospital

    Seoul, South Korea

    Key Differentiators

    • Radiology

  • #4

    University Hospital Zurich
    University Hospital Zurich

    Zurich, Switzerland

    Key Differentiators

    • Radiology

  • #5

    Duke University
    Duke University

    Durham, North Carolina

    Key Differentiators

    • Radiology

Original Source(s)

PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification

  • by Mithila Arman, Ahnaf Samin, A. K. M. Muzahidul Islam, Md Maruf Rusafi Arnob, Md Jahirul Islam, Ishtiak Al Mamoon

  • May 20, 2026

Related Content

Novel approaches for separating graft-versus-leukemia effects from graft-versus-host disease

Outcomes of critically ill pediatric patients after haploidentical stem cell transplantation

Gastric-type endocervical adenocarcinoma with Peutz-Jeghers syndrome: a case report and literature review