Analysis of Scalable and Efficient Approaches for Liver disease detection

Shaikh, Akimuddin Aslam (2024) Analysis of Scalable and Efficient Approaches for Liver disease detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

The liver disease detection using machine learning has seek significant attention due to its potential to improve diagnostic accuracy and patient outcomes. This study explores an unique approach by combining texture-based features that is Gray-Level Co-occurrence Matrix (GLCM), with high-dimensional pre-trained ResNet50 deep learning model. The research aims to address gaps identified in the literature, particularly the challenge of leveraging unlabeled medical imaging datasets.

The workflow encompassed machine learning models building steps like data preprocessing, feature extraction, clustering using unsupervised techniques (KMeans, Agglomerative Clustering), and supervised classification (Random Forest, SVM, XGBoost). The classification optimization followed 6 phased approach to assess the efficacy of feature sets and clustering technique. This phase include establishing baseline performance using extracted features from GLCM and ResNet50 features from the dataset that were tested both with Kmeans cluster and agglomerative cluster. ResNet50 features combined with Agglomerative cluster labels yielded near-perfect accuracy, achieving 99% with RF and SVM, and 100% with XGBoost outperforming ResNet50 features with KMeans labels. Among the finding, the GLCM features combined with KMeans cluster labels & agglomerative cluster labels yielded the near to perfect accuracy of 100% which indicate effective capture of the patterns in the feature but it is unusual in real-world scenario and indicated the sign of over-fitting. The study attempted to advanced the performance by Pseudo-labeling, generated from clustering, that facilitated the result of classifiers. The approach was followed by the integration of GLCM + ResNet50 features against Kmeans cluster and agglomerative clusters from which combine features of GLCM + ResNet50 with agglomerative target variable outperform kmeans target variable and enhanced classification performance, achieving up to 99% accuracy with the SVM classifier on combined features.

The results demonstrated the promising potential of combining texture and deep learning-based features in detecting liver diseases with high precision. This work provides a scalable framework for integrating diverse feature sets and contributes to the advancement of machine learning applications in medical imaging.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Diseases R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 14:20
Last Modified:	04 Sep 2025 14:20
URI:	https://norma.ncirl.ie/id/eprint/8798

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