Transfer Learning and AutoML as a Support for the Pneumonia Diagnosis Using Chest X-ray Scan

In the medical field, image classification is crucial for identifying respiratory diseases. Researchers propose combining the NIH Chest X-rays dataset with the Chest X-Ray Images dataset, creating a 14-class dataset including conditions like pneumonia, viral infections, and COVID-19. The goal is image classification. Given that an image can have multiple labels, the problem is treated as multi-label classification, transformed into a binary classification where each sample can have one or more labels. In AutoML, this task is framed as multiclass classification. The primary techniques employed are transfer learning and AutoML. Transfer learning fine-tunes pre-trained CNN models on the target dataset, while AutoML optimizes the entire process using automated pipelines. Existing studies differ in their focus, some employing transfer learning on all 14 classes, while others using AutoML for only three. Evaluation metrics like AUROC, precision, F1-score, and recall are used to assess performance, providing insights into accuracy and prediction quality. By comparing results from transfer learning and AutoML, researchers aim to determine the most effective approach for accurately classifying respiratory diseases in medical images.