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AI framework improves appendicitis grading from ultrasound images

By PulseAugur Editorial · [1 sources] ·

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 →

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

AI framework improves appendicitis grading from ultrasound images

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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]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman, Md Tahsin, Md. Nawab Yousuf Ali, Golam Sorwar ·

    AppendiGrade: An XAI-Enhanced Deep Learning Framework for Grading Appendicitis in Ultrasound with Gaussian Blur and Grad-CAM

    arXiv:2608.17923v1 Announce Type: cross Abstract: Appendicitis is one of the most common abdominal emergencies worldwide and requires prompt diagnosis and treatment to prevent life-threatening conditions. However, accurately differentiating complicated cases, such as perforation …

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