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Optimized Silicosis Detection Using a Feature-Enriched Multi-Model Ensemble Framework with An Enhanced U-Net Architecture

Year: 2025

Type: article

Abstract: Silicosis remains a major occupational health concern worldwide. This Research proposes an ensemble learning model combined with an improved U-Net for silicosis detection and classification. The dataset included 1300 healthy lung images and 1328 augmented silicosis images, split 80:20 for training and testing. The ensemble model integrated Inception V2, Inception V3, MobileNet, and ResNet50 for robust classification. Enhanced U-Net with skip connections and optimized hyperparameters achieved a validation accuracy of 98.33% and a Dice coefficient of 0.967. The ensemble outperformed standalone models, achieving an F1-score of 95.57%, a precision of 96.42%, and an MCC of 0.9275. Training was conducted using an NVIDIA i7 CPU, with R-Studio for analytics and visualization. Comparative analysis confirmed the model's superiority, aiding radiologists in faster decision-making.

Source: International Journal of Basic and Applied Sciences

DOI: 10.14419/3gepr148

Authors: Shivaanivarsha. N, Kavipriya. P

Institutions: Sathyabama Institute of Science and Technology, Sri Sairam Engineering College

Topic: COVID-19 diagnosis using AI

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4415959192

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