Enhancing Lung Disease Detection from Computed Tomography Images Using Deep Learning Algorithms

In the realm of medical imaging, timely diagnosis and therapy depend on the accurate detection of lung illnesses. This work mostly focuses on applying deep learning techniques, namely examining convolutional neural networks using popular designs such as FCN and LSTM. The model is trained on various datasets to optimize the capabilities of deep learning for accurate classification. Use effective training and evaluation techniques to learn image prioritization, image segmentation, and data curation in relation to model performance, including metrics such as sensitivity, accuracy, and specificity. This network’s main objective is to support picture segmentation, which supports image classification for the aim of illness prediction. The expected accuracy of the model gradually rises when labeled datasets are employed throughout the training process. The results demonstrate the importance of early detection in significantly improving patient outcomes in the diagnosis of lung disease, as well as the efficacy of the suggested procedures in attaining higher precision.