الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Objectives:
1. Develop a comprehensive CAD system for liver diseases utilizing data mining techniques.
2. Enhance accuracy, efficiency, and early detection in medical diagnostics through feature selection and image segmentation.
3. Address existing gaps and challenges in liver disease diagnosis through innovative algorithms and methodologies.
Methodology:
1. Conducted a literature review to contextualize CAD systems for liver disease diagnosis and identify challenges.
2. Introduced two novel metaheuristic algorithms: I-KOA for feature selection and SO-OBL for CT scan segmentation.
3. Demonstrated the effectiveness of these algorithms through experimental analysis and validation.
4. Integrated domain-specific knowledge to customize feature selection and segmentation procedures.
Results:
1. I-KOA showcased competitive performance in optimizing feature selection across diverse liver disease datasets, with strong predictive capabilities in classification models.
2. SO-OBL demonstrated high accuracy in CT scan segmentation, showcasing efficiency in addressing optimization challenges across various difficulty levels.
3. The findings underscored the transformative role of data mining techniques in developing sophisticated diagnostic tools for liver diseases.
Future Work:
1. Refine and extend the I-KOA approach, integrating domain-specific knowledge and broadening its applicability across diverse medical datasets.
2. Establish collaborative ventures with domain experts to optimize the classification model and ensure clinical relevance.
3. Leverage optimization algorithms to fine-tune classifier hyperparameters and enhance overall system performance.
4. Conduct real-world clinical validation to validate proposed methodologies using authentic clinical data.
5. Integrate deep learning techniques and multi-modal data sources to develop a comprehensive diagnostic model for liver diseases.
6. Explore data augmentation methods to enhance liver disease classification and evaluate their influence on system performance before integration.