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The increasing number of tumor cases has caused an alarming situation in the health care space. The tumor detection and diagnosis is a very computationally heavy and requires multiple medical imaging devices such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). It is very vital in order for early detection of tumor which can be done by precisely measuring their size which can improve a treatment by a huge factor in the patients. Existing diagnostic approaches often face challenges due to the diverse appearances of tumors and the constraints of current models. This study examines the different cutting-edge deep learning methods, with a focus on utilizing Generative Adversarial Networks (GANs) to enhance tumor identification across various categories, types, and imaging techniques. We also investigate the role of data augmentation strategies in enhancing the model performance. Furthermore, we examine the integration of Convolution Neural Networks (CNNs) to achieve accurate and robust results while preserving the data privacy. The goal of this study is to understand the detailed current scenario of the early detection and ways about the different techniques in various kinds of tumors which are present in the human body.

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Enhancing MRI Tumor Detection: A Survey on Image Upscaling, GAN Based Data Augmentation and Federated Learning in Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Gatla Vijayendher,
  • K. Sai Karthikeya,
  • E. Jayanth Madhav,
  • Tadepalli Satya Kiranmai

摘要

The increasing number of tumor cases has caused an alarming situation in the health care space. The tumor detection and diagnosis is a very computationally heavy and requires multiple medical imaging devices such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). It is very vital in order for early detection of tumor which can be done by precisely measuring their size which can improve a treatment by a huge factor in the patients. Existing diagnostic approaches often face challenges due to the diverse appearances of tumors and the constraints of current models. This study examines the different cutting-edge deep learning methods, with a focus on utilizing Generative Adversarial Networks (GANs) to enhance tumor identification across various categories, types, and imaging techniques. We also investigate the role of data augmentation strategies in enhancing the model performance. Furthermore, we examine the integration of Convolution Neural Networks (CNNs) to achieve accurate and robust results while preserving the data privacy. The goal of this study is to understand the detailed current scenario of the early detection and ways about the different techniques in various kinds of tumors which are present in the human body.