This paper presents a continuous initiative aimed at identifying cancerous nodules in Computed Tomography (CT) images. Given the size and resolution of these images, they are typically analyzed by radiologists, which can lead to increased fatigue and the potential for overlooking some cancerous lung nodules. This study introduces a novel Computer-Aided Detection (CAD) system designed to enhance the detection of cancerous nodules. The proposed algorithm consists of four distinct stages. Initially, an enhancement algorithm is applied to emphasize suspicious areas. In the subsequent stage, the regions of interest are identified. To minimize false positive detections, adaptive Support Vector Machine (SVM) and Wavelet transform techniques are employed. The algorithm has been tested and assessed using 60 CT images of both normal and cancerous lung nodules. The findings indicate that this innovative approach can effectively identify cancerous lung nodules, achieving a true positive rate of 94.5%, although it does result in a slightly elevated number of false positive regions, averaging 7 clusters per image.