Omiel, Lukas2026-09-162026-09-162026Omiel, L. (2026). Digital forensics framework for ensuring integrity and traceability of AI-based medical imaging : case study, breast cancer imaging data. [Unpublished dissertation]. Busitema University.https://bdears.busitema.ac.ug/handle/123456789/9562DissertationBreast cancer remains a serious worldwide health challenge and one of the leading causes of death in females. While Artificial Intelligence (AI) based medical imaging supports early disease detection, its reliability is currently compromised by lack of mechanisms to validate the integrity and traceability of images processed within the AI pipelines. This study addresses these issues by integrating cryptographic watermarking and hashing mechanism in a forensic by-design framework that performs AI-based medical image classification. The proposed framework performs content and context tamper detection as well as image classification through transfer learning using a pretrained Resnet50 Convolutional Neural Network (CNN). With a Peak Signal-to-Noise Ratio (PSNR) of 52.71 dB and a Structural Similarity Index (SSIM) of 0.9997, forensic evaluation shows that the framework guarantees tamper detection without affecting diagnostic quality. The maximum Integrity Detection Rate (IDR) of 100% was attained by the security layer alert, which also had the lowest False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 0%. Additionally, the findings of the experiment show a classification performance with an accuracy of 91%, a high Area Under the Curve (AUC) of 96.4%, and a benign recall of 94%, providing sensitivity in recognizing tissue that is not malignant. These findings show resilience to integrity violations during image classification. In contexts with limited resources, our hybrid method ensures that diagnostic data is authentic from the time of capture to the final clinical decision by providing the required foundation for AI forensics.enOther