An Novel Liver Tumor Using Mri Based Diagnosis on Artificial Intelligence and Machine Learning
Year: 2025
Type: article
Abstract: Liver cancer remains one of the most lethal malignancies globally due to late diagnosis and limited treatmentoptions. Magnetic Resonance Imaging (MRI) has emerged as a powerful non-invasive diagnostic tool, offeringsuperior soft tissue contrast and functional imaging capabilities for liver tumor detection and characterization.This study proposes a unique approach that leverages MRI scans integrated with artificial intelligence (AI) basedalgorithms to enhance the early diagnosis and accurate classification of liver cancer. The methodology involvespre-processing MRI images to reduce noise, followed by segmentation using advanced deep learning models likeU-Net and ResNet. These models are trained on annotated datasets to identify and differentiate benign frommalignant lesions with high precision. Furthermore, radiomic features extracted from segmented images are fedinto machine learning classifiers such as Random Forest and Support Vector Machine (SVM) to improvediagnostic accuracy. The proposed system demonstrates exceptional performance in identifying liver tumors,outperforming traditional diagnostic methods in sensitivity, specificity, and overall accuracy. By combiningimaging data with AI, the framework not only supports radiologists in clinical decision-making but also reducesinter-observer variability. This novel integration aims to revolutionize liver cancer detection, enabling earlyintervention and improved patient outcomes. Future directions include expanding the dataset diversity,incorporating multimodal imaging, and real-time deployment in clinical settings. The findings of this researchhighlight the transformative potential of AI-enhanced MRI analysis in liver oncology, pushing the boundaries ofpersonalized medicine and precision diagnostics.
Authors: Dr. K. Dharmarajan,, Dr. K. Abirami,, T. Haripriya,
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6949375112
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