Lung Disease Identification Using Machine Learning on HRCT Scans

Abstract

This study focuses on the classification of five lethal lung diseases along with normal lung conditions,COVID-19, pneumonia,TB,CLDs and ILDs. In collaboration with two doctors, a real-time dataset is gathered, using HRCT scans to identify the most revealing pathological characteristics of each disease. Based on the few-shot learning technique, a novel model is proposed and compared to established architectures such as MobileNetv3Small, MobileNetv3Large, ResNet18, and EfficientNet. Training the models from scratch reveals that our suggested model outperforms the other models with 99.47% accuracy, outperforming them (48%, 50%, 48.3%, and 49% accuracy, respectively). Furthermore, when implementing transfer learning to pretrain networks on ImageNet, the other models show promising results when utilized for few-shot learning. Due to overlapping effects and anomalies across different diseases, the study emphasizes the difficulties in precisely determining the root cause of lung ailments using HRCT scans. To address this, a real-world dataset is gathered to aid the research. The findings highlight the promising potential of few-shot learning techniques as well as the no need of large datasets for effective lung disease diagnosis and categorization.