Deep Learning Models for Tumor Detection and Segmentation in Medical Image Analysis: A Comprehensive Review of ResNet, U-Net, DETR, and Inception Variants

• Naseebia Khan,*  
• Abhinaba Das

Abstract

This survey paper delves into the realm of tumor detection and segmentation using deep learning models, focusing on the comparative performance of ResNet, U-Net, DETR, and Inception variants. Medical image analysis plays a pivotal role in clinical diagnosis, yet challenges in accuracy, efficiency, and consistency persist. Deep learning offers a solution by automating feature extraction and detection, thus improving diagnostic efficacy. ResNet harnesses its depth for intricate pattern recognition, while U-Net excels in segmenting small structures. DETR introduces transformer-based object detection, and Inception models balance accuracy and efficiency. Each model showcases unique advantages, alongside trade-offs in complexity and efficiency. The impact of these models on clinical practice and research is substantial. Their integration enhances patient care through early detection, personalized treatment plans, and precise localization. Researchers benefit from accelerated analysis of extensive datasets, yielding insights for tailored therapies. These models streamline clinical workflows, reducing the workload on medical professionals and enhancing patient outcomes. As deep learning continues to evolve, collaboration among healthcare experts, researchers, and data scientists remains pivotal. Ethical considerations, including data privacy and model transparency, are integral to responsible adoption. The path ahead is one of promise, where innovation, collaboration, and ethical considerations converge to drive the transformative potential of deep learning in tumor analysis.

Keywords

Convolutional Neural Network, Deep Learning, Tumor detection and segmentation, Medical Image analysis