Keywords: AI/ML Software, AI/ML Software
Motivation: Deep learning in medical imaging offers great potential, but accessibility for clinicians is challenging.
Goal(s): To develop an automated, user-friendly platform for seamless image preprocessing, model selection, training, evaluation, and interpretation.
Approach: Built in Python using MONAI, the platform includes features like image cropping, normalization, dataset splitting, and model selection (ResNet, DenseNet). Grad-CAM support enhances interpretability. The TCAI glioma dataset was used for testing.
Results: The platform achieved high accuracy on the TCAI dataset and is easy to use, making it accessible to clinicians without extensive technical expertise.
Impact: This platform enhances clinical accessibility to deep learning diagnostic tools, supporting high-precision diagnostics and interpretability through an intuitive interface, and reducing the technical barrier to AI in medical imaging.
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