In the current era of the introduction of artificial intelligence, there have been advances in the use of this field in image enhancement. The use of the histogram [local energy shape histogram (LESH)] approach based on local energy has previously helped diagnose breast cancer. The current support vector machine (SVM) algorithm is further advanced to AdaBoost algorithm for image extraction. The boosting algorithm of AdaBoost on the accuracy of the results will provide a much better result. For lung cancer diagnosis utilizing CT images, the LESH feature extraction algorithm is presented for lung cancer diagnoses using CT images [1]. This research builds on previous work by using the LESH with AdaBoost feature extraction methodology to detect lung cancer. The main objective of this research is to compare the LESH and HTF feature extraction approaches of SVM and AdaBoost. It is difficult to detect the specific symptoms of lung cancer since most cancer tissues are formed, and enormous tissue structures are crossed. Images will be evaluated using the LESA algorithms basic operation in this method. In this study, the GLCM technique is used to prepare snap photos and to evaluate the level of a patients condition at an early stage so that it may be established regularly or extraordinarily. The cancer stage is determined by the results. The survival rate of cancer patients can be determined using the dataset and results. The outcome is totally determined by the correct or erroneous arrangement of tissue patterning. Hence, a method must be such that it will remove the noise, extract vital information, and, at the same time, make it easy for a person to understand what is the problem with the given lung signal. In addition, the algorithm must have the ability to track the important changes and our approach should provide accurate, non-invasive assessment in clinical practice. After analyzing the signal, the method used provides vital information of linear methods, i.e., time domain and frequency domain parameters, and also provides the details of the indices of a cardiac patient and normal person which will be helpful to initiate treatment for a cardiac patient as soon as possible.