A STUDY ON ADVANCED IMAGE PROCESSING TECHNIQUES FOR DETECTING BRAIN METASTASIS TUMORS FROM RADIOLOGICAL IMAGES

Brain metastases, which are secondary tumors derived from primary malignancies, present major diagnostic difficulties because of their diverse morphology and imaging features. Conventional imaging methods, including MRI and CT, are based on manual interpretation, which is time-consuming and subjective. The current research investigates sophisticated image processing methods, combining deep learning models such as \'Convolutional Neural Networks\' (CNNs) to improve the accuracy of tumor detection. Comparative analysis showed that ResNet-50 attained the highest accuracy (94.2%), surpassing conventional approaches. The model presented here showed better segmentation with U-Net, with a Dice Similarity Coefficient of 0.89. Clinical verification ensured a 30% decrease in diagnostic time, highlighting the potential of AI-based frameworks to improve precision and efficiency in the detection of brain metastasis. Future research aims to enhance model performance using bigger datasets and multimodal imaging integration.