Lung Image Segmentation and Compression Using Deep Learning

Lung image compression and segmentation are essential diagnostic and monitoring techniques for a variety of respiratory ailments. We present a novel deep learning-based technique for segmenting and compressing lung images in this paper. To ascertain lung sections precisely in medical images, it is imperative to initially train a convolutional neural network (CNN) model. By understanding intricate patterns and features present in lung images, the CNN model is designed to identify and extract certain lung regions. This segmentation process saves medical staff a great deal of time and reduces the possibility of human error by eliminating the need for manual annotation. Following segmentation, lung image storage requirements are reduced without compromising diagnostic quality by employing a deep learning-based compression technique. The compression model's goal is to remove irrelevant and uninformative content while extracting the most important information. By employing deep learning, our compression technique achieves notable compression ratios while preserving essential information for accurate diagnosis. We conducted extensive trials with many lung imaging datasets to evaluate our proposed approach. When compared to traditional image processing methods, the results demonstrate increased segmentation accuracy and compression efficiency. We assessed the effectiveness of our method using several metrics, such as the compression ratio, mean squared error and coefficient of rolls. The proposed architecture for lung image segmentation and compression has great promise for enhancing respiratory illness identification and treatment. When segmentation is automated and lung picture compression is done rapidly, medical data may be sent, stored, and analyzed more efficiently, leading to better healthcare outcomes.