Journal of Engineering Research and Reports

Objectives: This study aims to develop a deep learning (DL)-based framework that enhances the detection and grading of liver Steatosis from ultrasound images. The key goal is to achieve accuracy levels comparable to experienced radiologists while maintaining interpretability and efficiency for real-time use in clinical practice.

Methods: The proposed system employs a multi-view ultrasound preprocessing approach, followed by transfer learning to leverage existing feature representations. Attention-driven convolutional neural networks (CNNs) are then used to capture fine details across image regions. To ensure clinical usability, explainability modules are integrated, allowing transparent interpretation of model predictions.

Findings: Experimental evaluation demonstrated that the framework outperformed traditional single-view methods, offering improved sensitivity and specificity in detecting liver Steatosis. The performance was closely aligned with radiologist-level assessments. Furthermore, the system showed low latency, highlighting its suitability for near real-time diagnostic applications.

Novelty: Unlike conventional models that rely on a single static image, this study introduces a multi-view fusion strategy enhanced with attention mechanisms and explainability tools. This combination not only strengthens predictive accuracy but also ensures transparency and trustworthiness—two critical factors for adoption in clinical settings. This Paper is significant as it introduces a reproducible deep learning framework that enhances the accuracy of liver Steatosis detection using ultrasound images. By combining multi-view imaging, attention mechanisms, and explainability tools, the work addresses limitations of traditional approaches and contributes to more reliable, transparent diagnostics. The study not only demonstrates performance comparable to radiologists but also emphasizes clinical adaptability, making it a practical step toward real-world healthcare applications. Ultimately, this research provides the scientific community with both a methodological advancement and a pathway to improve non-invasive liver disease screening.