A review: Lightweight architecture model in deep learning approach for lung disease identification

As one of the leading causes of death worldwide, early detection of lung disease is a very important step to improve the effectiveness of treatment. By using medical image data, such as X-ray or CT-scan, classification of lung disease can be done. Deep learning methods have been widely used to recognize complex patterns in medical images, but this approach has the constraints of requiring large data variations and high computing resources. In overcoming these constraints, the lightweight architecture in deep learning can provide a more efficient solution based on the number of parameters and computing time. This method can be applied to devices with low processor specifications on portable devices such as mobile phones. This article presents a comprehensive review of 23 research studies published between 2020 and 2025, focusing on various lightweight architectures and optimization techniques aimed at improving the accuracy of lung disease detection. The results show that these models are able to significantly reduce parameter sizes, resulting in faster computation times while maintaining competitive accuracy compared to traditional deep learning architectures. From the research that has been done, it can be seen that SqueezeNet applied on public COVID-19 datasets is the best basic architecture with high accuracy, and the number of parameters is 570 thousand, which is very low. On the other hand, UNet requires 31.07 million parameters, and SegNet requires 29.45 million parameters trained on CT scan images from Italian Society of Medical and Interventional Radiology and Radiopedia, so it is less efficient. For the combination method, EfficientNetV2 and Extreme Learning Machine (ELM) are able to achieve the highest accuracy of 98.20 % and can significantly reduce parameters. The worst performance is shown by VGG and UNet with a decrease in accuracy from 91.05 % to 87 % and an increase in the number of parameters. It can be concluded that the lightweight architecture can be applied to medical image classification in the diagnosis of lung disease quickly and efficiently on devices with limited specifications.