Enhanced Medical Tumor Detection

Medical Imaging plays a crucial role in diagnosing and treating various diseases, but relying on a single imaging modality often limits the depth of the information available to doctors. This paper introduces a pioneering deep learning-based approach to enhance the integration of multiple imaging techniques, like MRI, CT, and PET for more accurate diagnosis and treatment planning. By intelligently gathering data from these different imaging sources, our approach provides a more detailed and holistic view of medical conditions, helping doctors detect tumours more accurately and reduce diagnostic uncertainties. This advancement not only aids healthcare professionals in making more informed decisions but also paves the way for personalize treatment strategies. To increase accuracy and precision, enhanced medical tumour detection uses deep learning models, sophisticated imaging methods, and optimal data processing. While machine learning and deep learning models such as CNNs, Vision Transformers (ViTs), and hybrid models guarantee reliable tumour classification and segmentation, multi-modal imaging (MRI, CT, and PET) improves visualization. Techniques like ensemble learning, feature extraction, and data pretreatment improve detection performance even more. Precise boundary detection is ensured by accurate segmentation utilizing U-Net, DeepLabV3+, and Mask R-CNN. Model performance is validated by evaluation criteria like sensitivity, specificity, AUC-ROC, Jaccard Index, and dice coefficient. According to experimental findings, employing hybrid deep learning techniques ensures accurate medical tumour diagnosis with accuracy above 95% and precision above 92%.