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2025 Vol.16, Issue 3 Preview Page

General Article

Sustainable approaches in medical imaging: Enhanced MRI-based brain tumor classification using transfer learning for urban health applications
Ambuj Kathuria1
,
Deepali Gupta2*
,
Mudita Uppal3
1Research Scholar, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2Professor, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
3Assistant Professor, School of Computer Science and Engineering (SCSE), IILM University, Greater Noida, UP, India
*Corresponding Author
30 September 2025. pp. 355-372
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Abstract

Improving patient outcomes and enabling individualized treatment plans depend heavily on early brain tumor diagnosis. To customize treatments according to tumor kind, size, and location, brain tumors must be precisely segmented and classified. Even though deep learning and artificial intelligence have transformed the usage of magnetic resonance imaging (MRI) for identification of tumor, developing a reliable and extremely accurate model is still difficult. By using deep neural network topologies for multi-classification of brain tumor, this study tackles these issues. To improve the models’ robustness and generalizability, four benchmark datasets were combined for training and testing. The authors utilized metrics including accuracy, recall, precision and area under curve (AUC) to evaluate performance of ResNet101, VGG-16, VGG-19 and EfficientNetB0 models. ResNet101 outperformed other architectures such as VGG-16 (99.36%), VGG-19 (99.33%) and EfficientNetB0 (98.74%), with an outstanding accuracy of 99.94% among the tested models. These results demonstrate the potential of ResNet101 as a reliable method for automatically identifying and categorizing brain tumors in clinical environments. This study emphasizes sustainable approaches in medical imaging by leveraging transfer learning techniques to improve model efficiency and reduce computational costs. Furthermore, these advancements hold potential for urban health applications, offering scalable AI-driven diagnostic solutions for diverse healthcare infrastructures. In order to improve prognosis and enable individualized therapy, this cutting-edge system promises to expedite diagnostic procedures and allow medical practitioners to quickly and precisely identify brain tumors from MRI scans.

Keywords
brain tumor
classification
brain MRI
deep learning
convolutional neural network
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 16
  • No :3
  • Pages :355-372
  • Received Date : 2025-05-10
  • Accepted Date : 2025-08-02
  • DOI :https://doi.org/10.22712/susb.20250023
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