Applications of machine learning algorithms to support COVID-19 diagnosis using X-rays data information

• Published in: Expert systems with applications 2024-03, Vol.238, p.122029, Article 122029
• Main Authors: Medeiros, Elias P., Machado, Marcos R., de Freitas, Emannuel Diego G., da Silva, Daniel S., de Souza, Renato William R.
• Format: Article
• Language: English
• Subjects: COVID-19; Feature extraction; Machine Learning; Texture descriptor
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.122029

Due to the rapid spread of the new coronavirus variant during the most recent pandemic, it became difficult to distinguish common cold symptoms from those of other respiratory illnesses and coronavirus infections. X-rays of the thorax and Polymerase Chain Reaction with Reverse Transcription (RT-PCR) are common and efficient methods for preventing the spread of infectious diseases. In recent years, Machine Learning (ML) algorithms have been widely used to aid in the diagnosis of medical images, yielding simpler, more accurate, and quicker results. This study intends to apply texture descriptors to X-rays of COVID-19 patients’ lungs and utilize the extracted features in frameworks designed to accurately evaluate COVID-19 patients. Multiple experiments employing individual texture descriptors and their integration were conducted in order to incorporate these new characteristics into the proposed models. In addition, these frameworks will be compared to the conventional ML models used to aid in the diagnosis of COVID-19. When texture descriptors are used in conjunction with other standard features, the predictive power of the algorithms increases, according to the results. In addition, the accuracy increases when different types of texture descriptors are combined, resulting in enhanced metrics for detecting and diagnosing COVID-19. •We present new predictive frameworks to assist with medical image diagonals.•We evaluate texture extraction methods for aiding COVID-19 diagnosis.•We show that combining texture extraction methods enhances ML classifier accuracy.