Lung Cancer Detection Using Wavelet Transform with Deep Learning Algorithms

Abstract

Lung cancer is a leading cause of mortality among all cancer-related illnesses. The primary method of diagnosis is conducting a scan examination of the patient’s lungs. The scanning analysis can encompass X-ray, CT scan, or MRI techniques. The automated categorization of lung cancer poses a formidable challenge, primarily because of the diverse imaging techniques employed to capture images of a patient’s lungs. Image processing and machine learning methodologies have demonstrated significant promise in the identification and categorization of lung cancer. We present a very efficient model in this study that accurately detects lung cancer and categorizes it as either benign or malignant. The initial phase involves the execution of many procedures to carry out the picture preprocessing process. During the second stage, the image undergoes Wavelet Transform to divide it into three levels. This division allows for the extraction of distinct properties from each level. The third step involves employing an auto-encoder technique to effectively decrease dimensions and eliminate noise, while also identifying any anomalies within the recovered features. The MLP algorithm was employed in the final section. The suggested method underwent testing on a total of 9541 photos, which were categorized into two distinct types: benign, consisting of 4044 images, and malignant, consisting of 5497 images. The proposed approach attained a remarkable accuracy rate of 100%.