Due to the capability of generating data, generative modeling using deep learning methods has gained a lot of attention in the computer vision field. Medical deepfakes can cause serious resource drains in hospitals or even result in fatalities if they are not recognized. Injecting and removing tumors from medical scans is one method of deepfake generation used by the medical industry to generate data. This study intends to solve the problem of recognizing cancer manipulation and other relevant disease samples. In this study, multiple deep-learning models are trained to improve the classification performance of tampered lung cancer. We trained six CNN models, namely ResNet101, ResNet50, DensNet121, DenseNet201, MobileNetV2, and MobileNet. The models were trained using 2000 samples over three classes (Untampered, False-Benign, False-Malicious). We have enhanced the ResNet101 by adding more layers, achieving a training accuracy of 99%. The obtained results solved the problems that occurred in the first experiment considering Overfitting and Imbalanced dataset.