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In recent years, a brain tumour has become a fatal illness. Magnetic resonance imaging (MRI) is the primary method for diagnosing brain tumors and their extent. The advancements in Deep Learning techniques for computer vision have mainly been attributed to the abundance of training data and advancements in model designs, leading to improved accuracy in supervised environments. This study aims to enhance the capability and effectiveness of Magnetic resonance images for categorizing brain tumors. This paper utilizes pre-trained models ResNet50 and ResNet101 to train our dataset of brain tumors. Regarding their accuracy, this research compares the ResNet50 and ResNet101 models, as both address the same image classification task. The accuracy scores were 90% for ResNet50, and 93% for ResNet101, respectively. These accuracies enhance the early detection of tumors, potentially preventing physical consequences like paralysis and other impairments.

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Comparative Analysis of ResNet50 vs. ResNet101 Architectures in Brain Tumor Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Jatinder Kaur,
  • Ashutosh Kumar Singh,
  • Neeru Jindal

摘要

In recent years, a brain tumour has become a fatal illness. Magnetic resonance imaging (MRI) is the primary method for diagnosing brain tumors and their extent. The advancements in Deep Learning techniques for computer vision have mainly been attributed to the abundance of training data and advancements in model designs, leading to improved accuracy in supervised environments. This study aims to enhance the capability and effectiveness of Magnetic resonance images for categorizing brain tumors. This paper utilizes pre-trained models ResNet50 and ResNet101 to train our dataset of brain tumors. Regarding their accuracy, this research compares the ResNet50 and ResNet101 models, as both address the same image classification task. The accuracy scores were 90% for ResNet50, and 93% for ResNet101, respectively. These accuracies enhance the early detection of tumors, potentially preventing physical consequences like paralysis and other impairments.