Hafeez Ur Rehman

Hafeez Ur Rehman, Sumaiya Fazal, Moutaz Alazab et al.

Glioblastomas, constituting over 50% of malignant brain tumors, are highly aggressive brain tumors that pose substantial treatment challenges due to their rapid progression and resistance to standard therapies. The methylation status of the O-6-Methylguanine-DNA Methyltransferase (MGMT) gene is a critical biomarker for predicting patient response to treatment, particularly with the alkylating agent temozolomide. However, accurately predicting MGMT methylation status using non-invasive imaging techniques remains challenging due to the complex and heterogeneous nature of glioblastomas, that includes, uneven contrast, variability within lesions, and irregular enhancement patterns. This study introduces the Convolutional Autoencoders for MGMT Methylation Status Prediction (CAMP) framework, which is based on adaptive sparse penalties to enhance predictive accuracy. The CAMP framework operates in two phases: first, generating synthetic MRI slices through a tailored autoencoder that effectively captures and preserves intricate tissue and tumor structures across different MRI modalities; second, predicting MGMT methylation status using a convolutional neural network enhanced by adaptive sparse penalties. The adaptive sparse penalty dynamically adjusts to variations in the data, such as contrast differences and tumor locations in MR images. Our method excels in MRI image synthesis, preserving brain tissue, fat, and individual tumor structures across all MRI modalities. Validated on benchmark datasets, CAMP achieved an accuracy of 0.97, specificity of 0.98, and sensitivity of 0.97, significantly outperforming existing methods. These results demonstrate the potential of the CAMP framework to improve the interpretation of MRI data and contribute to more personalized treatment strategies for glioblastoma patients.

Sara Rehmat, Hafeez Ur Rehman

The overexpression of the human epidermal growth factor receptor 2 (HER2) in breast cells is a key driver of HER2-positive breast cancer, a highly aggressive subtype requiring precise diagnosis and targeted therapy. Immunohistochemistry (IHC) is the standard technique for HER2 assessment but is costly, labor-intensive, and highly dependent on antibody selection. In contrast, hematoxylin and eosin (H&E) staining, a routine histopathological procedure, offers broader accessibility but lacks HER2 specificity. This study proposes an advanced deep learning-based image translation framework to generate highfidelity IHC images from H&E-stained tissue samples, enabling cost-effective and scalable HER2 assessment. By modifying the loss function of pyramid pix2pix, we mitigate mode collapse, a fundamental limitation in generative adversarial networks (GANs), and introduce a novel variance-based penalty that enforces structural diversity in generated images. Our model particularly excels in translating HER2-positive (IHC 3+) images, which have remained challenging for existing methods due to their complex morphological variations. Extensive evaluations on the BCI histopathological dataset demonstrate that our model surpasses state-of-the-art methods in terms of peak signal-tonoise ratio (PSNR), structural similarity index (SSIM), and Frechet Inception Distance (FID), particularly in accurately translating HER2-positive (IHC 3+) images. Beyond medical imaging, our model exhibits superior performance in general image-to-image translation tasks, showcasing its potential across multiple domains. This work marks a significant step toward AI-driven precision oncology, offering a reliable and efficient alternative to traditional HER2 diagnostics.

Hafeez Ur Rehman, Anwar Ghani, Shehzad Ashraf Chaudhry et al.

Very recently, Barman et al. proposed a multi-server authentication protocol using fuzzy commitment. The authors claimed that their protocol provides anonymity while resisting all known attacks. In this paper, we analyze that Barman et al.'s protocol is still vulnerable to anonymity violation attack and impersonation based on the stolen smart attack; moreover, it has scalability issues. We then propose an improved and enhanced protocol to overcome the security weaknesses of Barman et al.'s scheme. The security of the proposed protocol is verified using BAN logic and widely accepted automated AVISPA tool. The BAN logic and automated AVISPA along with the informal analysis ensures the robustness of the scheme against all known attacks