arXiv:2606.27405v1 Announce Type: cross Abstract: Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans. Accurate and early diagnosis of brain tumors remains challenging due to the complexity of brain structures and reliance on manual interpretation. This work presents an automated deep learning-based approach for brain tumor detection from MRI images using Convolutional Neural Networks and Residual Networks. Transfer learning is applied with two pretrained architectures, ResNet18 and ResNet50, to classify MRI scans into tum
Advances in deep learning architectures and computational power are enabling more sophisticated applications in medical imaging analysis.
Automated, accurate disease detection using AI could significantly improve early diagnosis and treatment outcomes in critical health areas like oncology.
The reliance on manual interpretation of complex medical images will decrease as AI systems demonstrate enhanced reliability and efficiency.
- · AI healthcare tech companies
- · Oncology patients
- · Medical imaging diagnostics
- · Deep learning researchers
- · Traditional manual image interpretation services
Increased accuracy and speed of brain tumor diagnosis from MRI images.
Reduced healthcare costs through earlier detection and more effective treatment planning based on AI insights.
Further integration of AI into other medical diagnostic fields, leading to a paradigm shift in clinical practice and training.
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Read at arXiv cs.AI