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<p>A neurological disorder refers to any condition that disrupts the structure and normal functioning of the brain, spinal cord, or nerves, which are the key components of the nervous system. Detecting such disorders is often challenging, since symptoms frequently overlap, early indicators are often subtle, and the brain’s intricate functions differ greatly across individuals. Therefore, a new approach called Stacked Xception Convolutional Neural Network (SXcp-CNN) is devised for detecting brain neurological disorders utilizing Magnetic Resonance Imaging (MRI). Initially, the input MRI image is passed to noise reduction using the Median Filter to eliminate unwanted artifacts. Concurrently, Histogram Normalization is applied to enhance image contrast and improve visual quality. Afterwards, image augmentation is conducted by Generative Adversarial Networks (GAN), flipping, and rotation. Subsequently, image segmentation is accomplished by exploiting Znet with Weighted Tversky loss (WTL), developed by combining Weighted Binary Cross-Entropy loss (WBCE) and Focal Tversky loss. Next, the extraction of features is accomplished by exploiting EfficientNet-B0 and Haralick texture features. Finally, brain neurological disorder detection is done using the proposed SXcp-CNN, designed by integrating Stacked CNN (S-CNN), XceptionNet, and the Taylor concept. Furthermore, the proposed SXcp-CNN attains a better accuracy, True Positive Rate (TPR), True Negative Rate (TNR), F1-Score and precision of 96.703%, 97.148%, 96.263%, 96.405%, and 95.673%.</p>

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Hybrid Deep Learning Approach for Brain Neurological Disorder Detection from MRI Using Znet and Stacked Xception Convolutional Neural Network

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Shikha Shukla,
  • Alok Kumar

摘要

A neurological disorder refers to any condition that disrupts the structure and normal functioning of the brain, spinal cord, or nerves, which are the key components of the nervous system. Detecting such disorders is often challenging, since symptoms frequently overlap, early indicators are often subtle, and the brain’s intricate functions differ greatly across individuals. Therefore, a new approach called Stacked Xception Convolutional Neural Network (SXcp-CNN) is devised for detecting brain neurological disorders utilizing Magnetic Resonance Imaging (MRI). Initially, the input MRI image is passed to noise reduction using the Median Filter to eliminate unwanted artifacts. Concurrently, Histogram Normalization is applied to enhance image contrast and improve visual quality. Afterwards, image augmentation is conducted by Generative Adversarial Networks (GAN), flipping, and rotation. Subsequently, image segmentation is accomplished by exploiting Znet with Weighted Tversky loss (WTL), developed by combining Weighted Binary Cross-Entropy loss (WBCE) and Focal Tversky loss. Next, the extraction of features is accomplished by exploiting EfficientNet-B0 and Haralick texture features. Finally, brain neurological disorder detection is done using the proposed SXcp-CNN, designed by integrating Stacked CNN (S-CNN), XceptionNet, and the Taylor concept. Furthermore, the proposed SXcp-CNN attains a better accuracy, True Positive Rate (TPR), True Negative Rate (TNR), F1-Score and precision of 96.703%, 97.148%, 96.263%, 96.405%, and 95.673%.