Precision Prognosis in Oncology: Harnessing Deep Learning for Solid Tumor Imaging

Abstract

Recent technological advances in computer vision from Deep Learning (DL) have become a breakthrough technology that brings accurate detection and characterization of solid tumors, revolutionizing oncology. While traditional medical imaging techniques are becoming more and more advanced, they generally face issues when it comes to detecting the subtle patterns or anomalies that need early diagnosis for treatment planning. In this paper, we will focus on computational techniques such as Convolutional neural networks (CNN) which are used for effective tumor detection. We also discuss how pre-trained models like VGG19, ResNeXt and Inception help in transfer learning to leverage their power for the medical imaging datasets as well as the data augmentation. The practical application of these techniques in clinical contexts is presented with some real-world case studies. The results highlight the potential of deep learning in transforming oncology by paving the way for more personalized and precise medical interventions

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