Combating medical image tampering using deep transfer learning

Abstract

Tampered Medical images and scans are a very serious issue in the medical field. The results of tampered scans can range from mild to serious results. For example, a tampered scan or medical image can lead to misdiagnosing a patient with a serious condition or a patient who suffers from a serious condition can be misdiagnosed as healthy leading to a delay in receiving proper treatment. In this paper, we propose a deep learning-based methodology to detect tampered/fake cancers in 3D CT scans of human lungs. We use a publicly available dataset of deepfakes that include both tampered and genuine cancers and propose the use of a modified Convolutional Neural Network (CNN), in particular the AlexNet with Transfer Learning. The model was pre-trained on a large dataset and fine-tuned on the medical images dataset to detect tampering. Experimental results achieve a high level of accuracy in detecting tampering. Using the AlexNet with Transfer learning, we achieved an accuracy of 89.47%, Recall of 89.47%, Precision of 89.47%, and F1 measure of 89.47%. This is higher accuracy than similar methods using the same dataset reported in the extant literature. Achieving this high accuracy for a multiclass complex problem is considered an excellent achievement. Expert radiologists are mostly unable to distinguish between real and tampered cancer images. The results demonstrate the potential for deep learning-based models in improving accuracy and efficiency in tampered medical scan detection.