Lung Cancer Detection through TYDWT Algorithm

Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection of lung cancer plays a critical role in its treatment and survival rates. In recent years, computer-aided diagnosis systems have been developed to assist radiologists in detecting lung nodules in computed tomography (CT) images. This paper proposes a novel approach for lung cancer detection using Transverse Dyadic Wavelet Transform (TDWT) for feature extraction and classification. TDWT is a multi-resolution analysis technique that can capture both time and frequency information of the input images. The TYDWT algorithm is applied to the lung CT scan images to decompose the images into different sub-bands at multiple scales. The extracted features from these sub-bands are then used to train a machine learning model for lung cancer detection. The performance of the proposed method is evaluated on a publicly available dataset, achieving an accuracy of 95.6% and a sensitivity of 95.2%. The proposed method shows promising results for automated lung cancer detection, which can improve the accuracy and efficiency of the diagnosis process. The results demonstrate that the proposed approach using TDWT can be an effective method for early detection of lung cancer.