Evaluation of Machine/Deep Learning-Based Methods in Diagnosing Lung Diseases Using Radiographic Images

Lung diseases are one of the most important areas in medicine that significantly impact individuals' quality of life. Accurate diagnosis and appropriate timing of these diseases are crucial. In recent years, with the advancement of technology and the utilization of deep learning methods and neural networks, the diagnosis of lung diseases is performed relatively quickly and accurately. Utilizing the best technologies and algorithms can be effective in diagnosis and treatment of lung diseases. Therefore, investigating, comparing, and selecting the best algorithms for the development and advancement of future work is essential. Advanced technologies in online software and web-based platforms, along with medical knowledge, play a significant role in enhancing the diagnosis and treatment of lung diseases. This process not only aids lung health but also plays a fundamental role in prevention and treatment. This study introduces prominent and new architectures of machine and deep learning-based in lung disease diagnosis using radiographic images. It also examines the role of these architectures in increasing the accuracy, speed of lung disease diagnosis and presents advanced solutions for improving the process of lung disease diagnosis on the web. The obtained results suggest that deep learning methods perform significantly better than traditional feature-based classifiers.