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Brain tumors constitute some of the most serious and malignant diseases, and they drastically diminish the longevity of an individual. An inaccurate diagnosis by a medical expert may cause catastrophic repercussions and drastically lower a patient’s odds of survival. Consequently, an integrated team effort may contribute to a better diagnosis and prognosis. This study’s objective is to evaluate, using an innovative strategy, the Deep Learning-Unet sequence model’s capacity to foresee brain cancers using MRI data. Meningioma, glioma, and pituitary cancers were among the 7125 MRI photos in the analysis, as well as harmless scenarios. Utilizing extensive convolutional layering to derive key features using the pictures has yielded substantial results in statistics, including the F1 score, recollection, efficiency, and exactness. In just three instances (0.75, 0.81, and 0.85), the algorithm’s mean preciseness, recollection, and the F1 variables varied from 0.90 to 0.985. The results obtained suggest that the Deep Learning-Unet architecture has significant diagnostic utility in the realm of artificial intelligence domain. The study’s conclusions about the effectiveness of the models, tumor diagnoses, and possible future directions are extremely valuable.

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Novel Method for Assessing the Effectiveness of the Deep Learning-Based Unet Model in Forecasting Brain Tumors Using MRI Scans

  • 章节

  • 发表日期：

    2025年

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  • Gokapay Dilip Kumar,
  • Satyavarapu Khushi,
  • Seethama Reddy Gari Navya,
  • Kodavanti Sravya,
  • Sinduluri Tejasree

摘要

Brain tumors constitute some of the most serious and malignant diseases, and they drastically diminish the longevity of an individual. An inaccurate diagnosis by a medical expert may cause catastrophic repercussions and drastically lower a patient’s odds of survival. Consequently, an integrated team effort may contribute to a better diagnosis and prognosis. This study’s objective is to evaluate, using an innovative strategy, the Deep Learning-Unet sequence model’s capacity to foresee brain cancers using MRI data. Meningioma, glioma, and pituitary cancers were among the 7125 MRI photos in the analysis, as well as harmless scenarios. Utilizing extensive convolutional layering to derive key features using the pictures has yielded substantial results in statistics, including the F1 score, recollection, efficiency, and exactness. In just three instances (0.75, 0.81, and 0.85), the algorithm’s mean preciseness, recollection, and the F1 variables varied from 0.90 to 0.985. The results obtained suggest that the Deep Learning-Unet architecture has significant diagnostic utility in the realm of artificial intelligence domain. The study’s conclusions about the effectiveness of the models, tumor diagnoses, and possible future directions are extremely valuable.