AI framework improves appendicitis grading from ultrasound images

Researchers have developed AppendiGrade, a deep learning framework designed to improve the grading of appendicitis from ultrasound images. The system utilizes four pre-trained models, including InceptionV3, which achieved a significant performance boost to 95.58% accuracy after optimization techniques like image sharpening and hyperparameter tuning. To enhance interpretability, the framework employs Grad-CAM to generate heatmaps highlighting the regions of the ultrasound images that contribute most to the model's predictions, facilitating easier cross-checking with medical experts. AI

IMPACT Enhances diagnostic accuracy and interpretability in medical imaging, potentially leading to earlier and more precise treatment of appendicitis.

RANK_REASON The cluster contains a research paper detailing a new deep learning framework for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• AppendiGrade
• arXiv
• ConvNextTiny
• DenseNet201
• Gaussian blur
• Grad-CAM
• Hugging Face
• InceptionV3
• Omar Faruq Shikdar
• VGG19

• paper
• product

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