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CNNs Compared for Melanoma Detection Across Image Types

By PulseAugur Editorial · [1 sources] ·

A new research paper evaluates the effectiveness of several pre-trained convolutional neural networks (CNNs) for melanoma detection using both dermatoscopic and histopathological images. The study utilized datasets such as HAM10000, ISIC-2018, and CR-AI4SkIN, comparing architectures including ResNet50, VGG16, VGG19, MobileNet, and InceptionV3. Results indicated that ResNet50 performed best on dermatoscopic images with 84% accuracy on HAM10000, and also achieved 83% accuracy on histopathological images from CR-AI4SkIN. The research highlights that model performance varies significantly between the two image modalities. AI

IMPACT This research provides insights into selecting appropriate AI models for medical image analysis, potentially improving diagnostic accuracy in dermatology.

RANK_REASON The cluster contains an academic paper detailing a comparative evaluation of AI models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CNNs Compared for Melanoma Detection Across Image Types

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The cluster contains an academic paper detailing a comparative evaluation of AI models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Wagner Moreno Schmitz, Marco Antonio de Castro Barbosa, Thiago Magalh\~aes Amaral, Dalcimar Casanova, Jefferson Tales Oliva ·

    A Comparative Evaluation of Pre-trained Convolutional Neural Networks for Melanoma Detection

    arXiv:2609.11550v1 Announce Type: new Abstract: Early diagnosis of melanoma is critical for improving patient survival rates. However, accurately distinguishing melanoma from other skin lesions remains a significant clinical challenge due to the high visual similarity among lesio…

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