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<p>Brain tumors remain among the most dangerous life-threatening neurological disorders, and their accurate identification, segmentation, and classification are critical for improving treatment outcomes. Traditional cancer detection methods are often labor-intensive and susceptible to human error, whereas an automated solution is proposed to foster early diagnosis and reduce inaccuracies. This study presents FCDS-DeepVision, a lightweight convolutional neural network built from scratch to classify brain disorders using magnetic resonance imaging scans. The preprocessing stage applies a combination of intensity normalization, noise suppression, and contrast enhancement to improve the visibility of diagnostically relevant structures within the magnetic resonance imaging scans. To further reduce background interference, a segmentation step is introduced to localize anatomically meaningful brain regions prior to classification. This localized representation supports a more reliable separation of normal tissue from pathological patterns associated with glioma, meningioma, and pituitary tumors. The proposed framework is evaluated using a publicly available dataset of 7,020 brain magnetic resonance imaging scans collected from Kaggle. Experimental results indicate that the model maintains fast inference while producing stable and reproducible predictions across multiple evaluation metrics, including precision, recall, F1-score, Area Under the Curve, and overall accuracy. Rather than relying on a single performance indicator, the analysis emphasizes consistency across metrics under controlled experimental settings. The results suggest that such an automated pipeline can assist diagnostic workflows by reducing manual effort and limiting variability introduced through subjective interpretation. While the current findings are encouraging, further investigation is required to assess robustness under broader data distributions, larger cohorts, and real clinical acquisition conditions. Extending the dataset and validating the framework in practical diagnostic environments remain important directions for future work.</p>

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A lightweight deep learning framework for real time brain tumor detection and characterization using MR images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月11日

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  • Usman Ali,
  • Sajid,
  • Rehman Saleem,
  • Nabeel Imran,
  • Saeed Ali Omar Bahaj,
  • Tariq Mahmood,
  • Noor Ayesha

摘要

Brain tumors remain among the most dangerous life-threatening neurological disorders, and their accurate identification, segmentation, and classification are critical for improving treatment outcomes. Traditional cancer detection methods are often labor-intensive and susceptible to human error, whereas an automated solution is proposed to foster early diagnosis and reduce inaccuracies. This study presents FCDS-DeepVision, a lightweight convolutional neural network built from scratch to classify brain disorders using magnetic resonance imaging scans. The preprocessing stage applies a combination of intensity normalization, noise suppression, and contrast enhancement to improve the visibility of diagnostically relevant structures within the magnetic resonance imaging scans. To further reduce background interference, a segmentation step is introduced to localize anatomically meaningful brain regions prior to classification. This localized representation supports a more reliable separation of normal tissue from pathological patterns associated with glioma, meningioma, and pituitary tumors. The proposed framework is evaluated using a publicly available dataset of 7,020 brain magnetic resonance imaging scans collected from Kaggle. Experimental results indicate that the model maintains fast inference while producing stable and reproducible predictions across multiple evaluation metrics, including precision, recall, F1-score, Area Under the Curve, and overall accuracy. Rather than relying on a single performance indicator, the analysis emphasizes consistency across metrics under controlled experimental settings. The results suggest that such an automated pipeline can assist diagnostic workflows by reducing manual effort and limiting variability introduced through subjective interpretation. While the current findings are encouraging, further investigation is required to assess robustness under broader data distributions, larger cohorts, and real clinical acquisition conditions. Extending the dataset and validating the framework in practical diagnostic environments remain important directions for future work.