Lukas Kühn, Lara Lindert, Wiebke Glawe, Kyung-Eun Anna Choi
[Determinants of use of rehabilitation services before entering disability pension: Time series analysis of insured members of the German Pension Insurance Federation in Berlin and Brandenburg]
Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))
brain cancer lifethreatening disease requiring close attention early accurate diagnosis using noninvasive medical imaging critical successful treatment patient survival however manual diagnosis radiologist experts timeconsuming limitations processing large datasets efficiently therefore efficient systems capable analyzing vast amounts medical data early tumor detection urgently needed deep learning dl deep convolutional neural networks dcnns emerges promising tool understanding diseases like brain cancer medical imaging modalities especially mri provides detailed soft tissue contrast visualizing tumors organs dl techniques become popular current research brain tumor detection unlike traditional machine learning methods requiring manual feature extraction dl models adept handling complex data like mris excel classification tasks making wellsuited medical image analysis applications study presents novel dual dcnn model accurately classify cancerous noncancerous mri samples dual dcnn model uses two wellperformed dl models ie inceptionv3 densenet121 features extracted models appending global max pooling layer extracted features utilized train model addition five fully connected layers finally accurately classify mri samples cancerous noncancerous fully connected layers retrained learn extracted features better accuracy technique achieves 99 99 98 99 accuracy precision recall f1scores respectively furthermore study compares dual dcnns performance various wellknown dl models including densenet121 inceptionv3 resnet architectures efficientnetb2 squeezenet vgg16 alexnet lenet5 different learning rates study indicates proposed approach outperforms established models terms performance