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Artificial Intelligence and Deep Learning for Early Detection and Diagnosis of Brain Tumors in Neurosurgery Using CT Scan and Medical Imaging
ABSTRACT
Background:
Lung cancer remains a leading cause of mortality worldwide, with early detection significantly improving survival rates. Computed tomography (CT) scans are a primary imaging modality for identifying lung tumors, but manual interpretation can be time-consuming and prone to errors. Deep learning models offer promise for automating and enhancing detection accuracy.
Methods:
We developed a hybrid deep learning framework combining convolutional neural networks (CNNs) for classification and object detection models like YOLOv8 for localization. The model was trained on publicly available datasets, including the Lung-PET-CT-Dx and Kaggle Chest CT-Scan images, comprising over 20,000 CT images. Preprocessing involved image resizing, augmentation, and segmentation techniques such as Watershed and Otsu's thresholding. Hyperparameter tuning was performed using grid search, with evaluation on metrics like accuracy, precision, recall, F1-score, and Jaccard index.
Results:
Our model achieved 98.5% accuracy in classifying CT images as cancerous or non-cancerous, with 90.3% precision in tumor localization. This enabled detection of tumors at earlier stages compared to traditional methods, reducing false negatives by 15%.
Conclusions:
The integration of deep learning with CT imaging facilitates earlier tumor detection, potentially improving patient outcomes. Future work should focus on multi-center validation and real-time clinical integration.
Citation
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Copyright
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