Dual concatenated transfer learning with attention fusion: An ensemble-enhanced approach for skin lesion classification - Takeaways - MDSpire
Coming Soon: Introducing MDSpire News. Learn more
Conexiant’s news site is now MDSpire News. Learn more

Ensemble-Enhanced Skin Lesion Classification Using Dual Concatenated Transfer Learning with Attention Fusion

  • By

  • Probal Bhowmick

  • Julia Rahman

  • Anwar Hossain Efat

  • Tasfi Fairoz Nidhi

  • Dipanjan Karmaker Amit

  • June 8, 2026

Share

  • 1

    Skin lesions are critical indicators of dermatological disorders, with melanoma being the most lethal skin cancer type.

  • 2

    AI, particularly ML and DL, can enhance self-screening for skin cancer, addressing the challenges of high consultation costs.

  • 3

    The study introduced a hybrid multi-branch ensemble framework combining DenseNet and ResNet variants to improve classification performance.

  • 4

    Attention mechanisms were integrated to enhance feature extraction and reduce background noise in skin lesion images.

  • 5

    A comprehensive evaluation using multiple metrics was conducted to ensure model validity, particularly for imbalanced datasets.

Original Source(s)

Dual concatenated transfer learning with attention fusion: An ensemble-enhanced approach for skin lesion classification

  • by Probal Bhowmick, Julia Rahman, Anwar Hossain Efat, Tasfi Fairoz Nidhi, Dipanjan Karmaker Amit

  • June 8, 2026

Related Content

SCC-Net: A lightweight attention-enhanced deep learning model for automated squamous cell carcinoma detection

Sunscreen Myths Draw TikTok Engagement

Health-related critiques of sunscreen were uncommon, but videos containing critique drew more audience interaction than promotion-only videos.

Classification of High- and Low-Risk Patients With Dermal Leiomyosarcoma: An Exploratory Nationwide Cohort Study