A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection.
Authors
Affiliations (7)
Affiliations (7)
- Department of Computer Science & Engineering, IIT Patna, Patna, India.
- Department of Computer Science & Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India. [email protected].
- Department of Chemistry, University of Kashmir, Srinagar, Jammu & Kashmir, India.
- Department of Computer Science & Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India.
- Amity School of Engineering & Technology, Amity University Haryana, Gurgaon, India. [email protected].
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
- Department of Computer Engineering, Inha University, Incheon, Republic of South Korea. [email protected].
Abstract
Segmenting liver tumors in medical imaging is pivotal for precise diagnosis, treatment, and evaluating therapy outcomes. Even with modern imaging technologies, fully automated segmentation systems have not overcome the challenge posed by the diversity in the shape, size, and texture of liver tumors. Such delays often hinder clinicians from making timely and accurate decisions. This study tries to resolve these issues with the development of UIGO. This new deep learning model merges U-Net and Inception networks, incorporating advanced feature selection and optimization strategies. The goals of UIGO include achieving high precision segmented results while maintaining optimal computational requirements for efficiency in real-world clinical use. Publicly available liver tumor segmentation datasets were used for testing the model: LiTS (Liver Tumor Segmentation Challenge), CHAOS (Combined Healthy Abdominal Organ Segmentation), and 3D-IRCADb1 (3D-IRCAD liver dataset). With various tumor shapes and sizes ranging across different imaging modalities such as CT and MRI, these datasets ensured comprehensive testing of UIGO's performance in diverse clinical scenarios. The experimental outcomes show the effectiveness of UIGO with a segmentation accuracy of 99.93%, an AUC score of 99.89%, a Dice Coefficient of 0.997, and an IoU of 0.998. UIGO demonstrated higher performance than other contemporary liver tumor segmentation techniques, indicating the system's ability to enhance clinician's ability to deliver precise and prompt evaluations at a lower computational expense. This study underscores the effort towards advanced streamlined, dependable, and clinically useful devices for liver tumor segmentation in medical imaging.