Comparative Analysis of Deep Learning Models for Tumor Detection in Medical Imaging
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Abstract
The rapid advancements in deep learning (DL) have revolutionized tumor detection in medical imaging, offering significant improvements in diagnostic accuracy and efficiency. This paper presents a comparative analysis of several state-of-the-art deep learning models for tumor detection in medical imaging, focusing on their performance across various datasets, including CT scans, MRIs, and X-rays. We explore commonly used architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, evaluating their strengths and weaknesses in terms of detection accuracy, processing time, robustness to noise, and generalizability across different imaging modalities. The study also highlights the role of data augmentation, transfer learning, and model fine-tuning in enhancing the models' effectiveness. By providing an in-depth comparison of these models, this paper aims to guide clinicians and researchers in selecting the most suitable deep learning approaches for tumor detection tasks, while also addressing the challenges associated with real-world implementation in healthcare settings.
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