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Joint classification and regression with deep multi task learning model using conventional based patch extraction for

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Summary

A novel Deep Multi-Task Convolutional Neural Network (DMTCNN) accurately diagnoses brain diseases using MRI scans. This AI model improves diagnostic accuracy and efficiency for better patient care.

Keywords:
Brain diseaseClassificationConvolutional neural networksDeep multi-task convolutional neural networkMagnetic resonance imagingMulti task learningRegression

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Neurology and Diagnostic Medicine

Background:

  • Accurate diagnosis of brain diseases is crucial for effective treatment planning and patient care, with Magnetic Resonance Imaging (MRI) playing a key role.
  • Current computer-aided diagnosis methods often rely on manually engineered features from MRI, limiting their efficiency and adaptability.
  • Deep Multi-Task Convolutional Neural Network (DMTCNN) is proposed to overcome these limitations in brain disease diagnosis.

Purpose of the Study:

  • To develop and validate an effective iterative method for non-smooth optimization problems in brain disease diagnosis.
  • To present a Deep Multi-Task Convolutional Neural Network (DMTCNN) for combined regression and classification of brain diseases.
  • To enhance the utilization of shared information and improve overall performance through cooperative learning of multiple diagnostic tasks.

Main Methods:

  • Utilized a Deep Multi-Task Convolutional Neural Network (DMTCNN) for simultaneous regression and classification tasks related to brain diseases.
  • Employed an edge detector, specifically the Canny edge detector, for pre-processing MRI images.
  • Extracted discriminative anatomical features from image patches using convolutional neural networks (CNNs) within a multi-task learning (MTL) framework.

Main Results:

  • The DMTCNN model demonstrated significant improvements in key performance metrics, including specificity (94.18%), sensitivity (93.19%), accuracy (96.97%), and F1-score (94.18%).
  • Achieved a Root Mean Square Error (RMSE) of 22.76% and an execution time of 4.875 ms, showcasing high efficiency.
  • Verified the capability of DMTCNN to accurately categorize dissimilar brain disorders, outperforming state-of-the-art techniques.

Conclusions:

  • The proposed DMTCNN is an effective and efficient method for the computer-aided diagnosis of brain diseases using MRI.
  • Multi-task learning enhances model generalization and diagnostic accuracy, even with limited labeled data.
  • DMTCNN shows superior performance in precisely identifying various brain diseases compared to existing methods.