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