Md Rafid Islam

Md Rafid Islam, Rafsan Jany, Akib Ahmed et al.

Diabetic retinopathy (DR) remains a leading cause of preventable blindness, yet large-scale screening is constrained by limited specialist availability and variable image quality across devices and populations. This work investigates whether feature-level fusion of complementary convolutional neural network (CNN) backbones can deliver accurate and efficient binary DR screening on globally sourced fundus images. Using 11,156 images pooled from five public datasets (APTOS, EyePACS, IDRiD, Messidor, and ODIR), we frame DR detection as a binary classification task and compare three pretrained models (ResNet50, EfficientNet-B0, and DenseNet121) against pairwise and tri-fusion variants. Across five independent runs, fusion consistently outperforms single backbones. The EfficientNet-B0 + DenseNet121 (Eff+Den) fusion model achieves the best overall mean performance (accuracy: 82.89\%) with balanced class-wise F1-scores for normal (83.60\%) and diabetic (82.60\%) cases. While the tri-fusion is competitive, it incurs a substantially higher computational cost. Inference profiling highlights a practical trade-off: EfficientNet-B0 is the fastest (approximately 1.16 ms/image at batch size 1000), whereas the Eff+Den fusion offers a favorable accuracy--latency balance. These findings indicate that lightweight feature fusion can enhance generalization across heterogeneous datasets, supporting scalable binary DR screening workflows where both accuracy and throughput are critical.

Md Rafid Islam

Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another. This paper presents a comprehensive study on the generalizability and interpretability of permission-based detectors under cross-domain conditions. Using two complementary datasets (PerMalDroid and NATICUSdroid) and five ensemble classifiers, we first establish an intra-domain baseline, where models achieve over 92% accuracy, and then quantify a severe asymmetric performance drop. While models trained on PerMalDroid generalize well to NATICUSdroid (86% accuracy), the reverse direction sees a drastic drop to 73% accuracy. Explainable AI analysis reveals bimodal feature distributions and shows that feature importance is highly unstable, with key permissions losing or gaining influence across domains. The predictive feature sets for different domains are fundamentally mismatched, as models rely on different, dataset-specific permissions. Most importantly, an ablation study demonstrates that for most models, training on a noisy feature set leads to poor generalization, confirming that domain-specific artifacts are a greater obstacle than missing features. To mitigate this, we validate a hybrid training strategy based on the intersection of common features and successfully recover cross-domain performance, achieving 88% accuracy on PerMalDroid and maintaining 97% on NATICUSdroid. These findings highlight the importance of explainable, cross-domain-robust malware detection systems and provide a practical pathway toward improving real-world deployment of permission-based Android malware detectors.