Md Ashik Khan

Fixed-Threshold Evaluation of a Hybrid CNN–ViT for AI-Generated Image Detection Across Photos and Art

Md Ashik Khan, Arafat Alam Jion

2025 28th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2025, pp. 3853–3858 · doi: 10.1109/ICCIT68739.2025.11491484

We introduce a fixed-threshold evaluation protocol that holds decision thresholds fixed across all post-processing transformations (JPEG compression, blur, downscaling), exposing a forensic-semantic spectrum: frequency-aided CNNs collapse under compression (93.33% → 61.49%) while ViTs degrade minimally (92.86% → 88.36%).

Fixed-Budget Parameter-Efficient Training with Frozen Encoders Improves Multimodal Chest X-Ray Classification

Md Ashik Khan, Md Nahid Siddique

2025 28th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2025, pp. 2748–2753 · doi: 10.1109/ICCIT68739.2025.11491458

Under a fixed parameter budget (2.37M, 2.51% of total), PET variants (BitFit, LoRA, adapters) achieve AUROC 0.892–0.908, outperforming full fine-tuning (0.770 AUROC) which uses 94.3M trainable parameters — a 40× reduction.

Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification

Md Ashik Khan, Rafath Bin Zafar Auvee

2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 639–644 · doi: 10.1109/ICCIT64611.2024.11021970

Cited by 34

Accurate brain tumor classification in MRI images is essential for timely diagnosis. Our custom CNN achieved 98.67% accuracy on the Br35H dataset and 99.62% on the Brain Tumor MRI Dataset, showing competitive performance against pre-trained ResNet18 and VGG16 at lower complexity.