Enhancing Lung Disease Diagnosis through Meta Learning: A Framework Utilizing Few-Shot Learning Techniques

Ailments of the lungs stand out as the most common medical conditions that are encountered in the healthcare industry. Manual assessment, which frequently involves the use of chest X-ray images and visual inspection, is something that is commonly used in traditional diagnostic approaches. This method can be time-consuming and prone to errors. Deep learning techniques, despite their potency, require a significant quantity of data in order to achieve optimal performance. These techniques frequently produce unsatisfactory results when applied to tiny datasets. In order to solve these issues, the research being conducted here intends to make use of a meta-learning technique, more precisely few-shot learning. CLAHE, which stands for contrast limited adaptive histogram equalization, is used to do the initial preprocessing of the raw dataset in the framework that has been proposed. A segmentation procedure is performed on the pre-processed images by utilizing the UNET model, which guarantees accurate separation of lung nodules. After the nodules have been segmented, they are subjected to feature extraction by transfer learning. This is accomplished by employing the CheXNet model that has been pre-trained on the Chest Xray14 dataset, which contains 14 chest abnormalities. After the features have been extracted, they are transferred into a classifier, which is then used to distinguish between a variety of abnormalities and normal circumstances. The new component of this research resides in the implementation of few-shot learning techniques within the classifier, allowing efficient image categorization even with insufficient data. A promising strategy to intensifying the precision and efficiency of lung disease diagnosis in the healthcare sector is presented by the technique that has been developed.