Harnessing Artificial Intelligence in Medical Imaging for Enhanced Cancer Detection and Diagnosis

Abstract

The integration of Artificial Intelligence (AI) into medical imaging has revolutionized cancer detection and diagnosis, offering unprecedented accuracy, speed, and consistency. This study investigates the application of advanced AI models, particularly Convolutional Neural Networks (CNNs), in analyzing medical images for enhanced identification of cancerous tissues. Models including VGG16, ResNet50, and DenseNet121 were evaluated for classification tasks, while U-Net variants were utilized for segmentation. A comprehensive methodology encompassing data collection, preprocessing, augmentation, and evaluation was employed to ensure robustness. Experimental results revealed that DenseNet121 achieved the highest performance across precision, recall, and F1-score metrics. Graphical and tabular analyses further validated model efficacy and computational efficiency. This research highlights the significant potential of deep learning in clinical oncology and sets the stage for future developments involving multimodal data integration, real-time AI deployment, and explainable models for enhanced clinical trust. The findings affirm AI’s transformative role in medical imaging and pave the way for its adoption in real-world cancer diagnosis systems.