A Novel Method to Detect Lymphangioleiomyomatosis (LAM) Using CNN- LSTM on Computed Tomography

Detection of Lymphangioleiomyomatosis (LAM) using CNN-LSTM framework on CT images, Lymphangioleiomyomatosis (LAM) is a progressive lung disease marked by uncommon cystic growths, often diagnosed using high-resolution CT scan images. The complex nature of LAM's radiological findings and the need for specialized knowledge make early and correct diagnosis difficult, even though it is essential for successful treatment. In order to automate the detection of LAM in CT images, this study introduces an advanced deep learning approach that integrates CNN with LSTM networks. A well-annotated dataset of CT scans from patients with LAM and healthy controls was used to create and evaluate the model. To increase model robustness and generalizability, extensive preprocessing was used, including lung area segmentation and data augmentation. Accuracy, sensitivity, specificity, and AUC-ROC were among the key performance indicators that demonstrated how well the model distinguished between LAM and non-LAM cases. The accuracy of the CNN-LSTM model for detecting Lymphangioleiomyomatosis (LAM) using CT scan images is approximately 80. By providing a dependable, non-invasive, and scalable approach to early LAM identification, this novel CNN-LSTM design lessens the need for expert interpretation and improves diagnostic effectiveness.