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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Updated: Jun 23, 2025

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Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.

M Padma Usha1, G Kannan1, M Ramamoorthy2

  • 1Department of Electronics and Communication Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India.

Behavioural Neurology
|June 17, 2024
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Summary

This study introduces Tumnet, a deep learning method for brain tumor classification and segmentation using fused MRI and CT images. Tumnet achieves high accuracy, improving diagnosis and patient care for aggressive brain malignancies.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain malignancies are aggressive tumors with poor prognoses, necessitating effective diagnostic and therapeutic strategies.
  • Medical imaging modalities like MRI, PET, and CT are crucial for brain tumor diagnosis and treatment planning.
  • Accurate tumor classification and segmentation are vital for improving patient outcomes.

Purpose of the Study:

  • To propose a deep learning-based multimodal fused imaging approach for brain tumor classification and segmentation.
  • To evaluate the performance of the proposed Tumnet technique using fused MRI and CT images.
  • To compare the efficacy of Tumnet on both multimodal and single-modal (MRI/CT) brain tumor images.

Main Methods:

  • Utilized pixel-level fusion of 308 MRI and CT brain tumor slices (meningioma and sarcoma) using three distinct methods.
  • Developed and applied the Tumnet deep learning model, comprising 5 convolutional, 3 pooling, and 3 fully connected layers with ReLU activation.
  • Performed classification and segmentation of brain tumors on fused multimodal images and single-modal MRI/CT images (561 slices).

Main Results:

  • First-order statistical fusion metrics (average method) showed SSIM tissue at 83%, SSIM bone at 84%, accuracy at 90%, sensitivity at 96%, and specificity at 95%.
  • Second-order statistical fusion metrics revealed a standard deviation of fused images at 79% and entropy at 0.99, indicating enhanced features.
  • The Tumnet model achieved high performance on fused images: 96% sensitivity, 98% accuracy, 99% specificity, with normalized mean 0.75, standard deviation 0.4, variance 0.16, and entropy 0.90.

Conclusions:

  • Multimodal fused imaging combined with the Tumnet deep learning model significantly enhances brain tumor classification and segmentation.
  • The proposed Tumnet technique demonstrates superior performance, offering a promising tool for improved brain tumor diagnosis.
  • The findings suggest that deep learning-based fusion of MRI and CT images can lead to more accurate and reliable detection of brain tumors.