چکیده مقاله
In this study, we present a novel explainable AI XAI pipeline for brain tumor diagnosis from medical images Our approach integrates a convolutional neural network CNN based classifier with Gradient weighted Class Activation Mapping Grad CAM to localize suspicious regions in brain MRI scans We extract critical information such as the tumor type and probability score and convert it into structured textual prompts These prompts are then fed into MedAlpaca 7B, a biomedical language model, to generate natural language explanations of the tumor diagnosis The proposed system enhances interpretability by bridging visual model outputs with human understandable medical narratives, offering support for radiologists in clinical decision making and improving transparency in AI assisted diagnostics By offering early and interpretable insights, our system contributes not only to accurate diagnosis but also to timely intervention and secondary prevention of cancer progression, helping clinicians monitor and manage high risk cases before they worsen This positions our method as a valuable tool in AI powered early response strategies for brain tumor management
کلیدواژهها
نویسندگان
Seyed Ali Nedaaee Oskoee
Department of Computer Engineering, University of Zanjan, Zanjan ۴۵۳۷۱ ۳۸۷۹۱, Iran
Leila Safari
Department of Computer Engineering, University of Zanjan, Zanjan ۴۵۳۷۱ ۳۸۷۹۱, Iran
Seyed Ehsan Nedaaee Oskoee
Department of Physics, Institute for Advanced Studies in Basic Sciences, Zanjan ۴۵۱۳۸ ۶۶۷۳۱, Iran
شیوه ارجاع
Nedaaee Oskoee, Seyed Ali and Safari, Leila and Nedaaee Oskoee, Seyed Ehsan,1404,XAI-Driven Early Detection and Prevention of Brain Tumors: Integrating Grad-CAM and MedAlpaca-7b for Interpretable Medical Imaging Reports,The Second International Congress of Cancer,Zanjan
ارائهشده در
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