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A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network
B Sumathy1, Pankaj Dadheech2, Monika Jain3
1Department of Instrumentation and Control Engineering, Sri Sairam Engineering College, Chennai, India.
Journal of Healthcare Engineering
|March 10, 2022
Summary
This study introduces a new method for classifying liver diseases using CT scans, achieving high accuracy. The approach effectively segments the liver and identifies cancerous regions, improving diagnostic capabilities.
Area of Science:
- Medical Imaging
- Computational Pathology
- Hepatology
Background:
- The liver is a vital organ responsible for numerous metabolic and detoxification functions.
- Accurate classification of liver diseases is crucial for effective patient management.
- Distinguishing the liver from surrounding organs in medical images presents a significant challenge.
Purpose of the Study:
- To develop and evaluate a novel, noninvasive method for liver disease classification using computed tomography (CT) scans.
- To improve the accuracy and efficiency of liver disease diagnosis by automating segmentation and categorization processes.
Main Methods:
- Utilized the Partial Differential Technique (PDT) for liver segmentation from surrounding organs.
- Employed Level Set Methodology (LSM) to delineate cancerous regions within the liver.
- Implemented an Improved Convolutional Classifier for the final categorization of liver disease stages.
Main Results:
- Achieved a high performance accuracy of 97.5% for liver categorization.
- Demonstrated excellent sensitivity (96%) and specificity (93%) compared to existing algorithms.
- Reported a low error rate of 2.1% with a 94.5% confidence interval for accuracy.
Conclusions:
- The proposed method offers a highly accurate and reliable noninvasive approach for liver disease classification.
- The integration of PDT, LSM, and an Improved Convolutional Classifier enhances diagnostic capabilities in medical imaging.
- This technique shows significant potential for improving the early detection and management of liver pathologies.
