Beatrice Emma-Okon, Michal Tombs, Rufus Akomolafe, Olugbenga Ayannuga
Teaching pre-clinical medical students remotely in Nigeria post Covid-19 pandemic: can past experiences shape future directions?
BMC medical education
study addresses critical challenge detecting brain tumors using mri images pivotal task medical diagnostics demands high accuracy interpretability deep learning shown remarkable success medical image analysis remains substantial need models accurate also interpretable healthcare professionals existing methodologies predominantly deep learningbased often act black boxes providing little insight decisionmaking process research introduces integrated approach using resnet50 deep learning model combined gradientweighted class activation mapping gradcam offer transparent explainable framework brain tumor detection employed dataset mri images enhanced data augmentation train validate model results demonstrate significant improvement model performance testing accuracy 9852 precisionrecall metrics exceeding 98 showcasing models effectiveness distinguishing tumor presence application gradcam provides insightful visual explanations illustrating models focus areas making predictions fusion high accuracy explainability holds profound implications medical diagnostics offering pathway towards reliable interpretable brain tumor detection tools