Background/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy. Methods: The proposed method (PM) combines modified Inception modules, Depthwise Squeeze-and-Excitation Blocks, and ConvMixer architecture. Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. Key performance metrics, including accuracy, precision, recall, and F1 score, were calculated to assess the method’s diagnostic performance. Results: On the private dataset, the PM achieved outstanding performance: 97.98% accuracy, 97.95% precision, 97.77% recall, and 97.86% F1 score. When tested on the public Noor dataset, the method reached 100% across all evaluation metrics, outperforming existing DL approaches. Conclusions: These results highlight the promising role of AI-based systems in AMD diagnosis, of-fering advanced feature extraction capabilities that can potentially enable early detection and in-tervention, ultimately improving patient care and outcomes. While the proposed model demon-strates promising performance on the datasets tested, the study is limited by the size and diversity of the datasets. Future work will focus on external clinical validation to address these limita-tions.

Bu makalede, dünyada görme bozukluklarının %8.7’sinin sebebi olan yaşa bağlı makula dejenerasyonu hastalığının tespit edilmesi için otomatik bir sistem sunulmuştur. Hastalığın teşhisi için, son zamanlarda görüntüden özellik tanısı çıkarma işlemlerinde sıklıkla kullanılan evrişimsel sinir ağı mimarisinden faydalanılmıştır. Çalışmada, yaşa bağlı makula dejenerasyonu hastalığının sınıflandırılması için evrişimsel sinir ağı, etiketli 28031 adet OCT görüntüsü ile eğitilmiştir. Eğitilen evrişimsel sinir ağı 6900 adet OCT görüntüsü ile test edilmiştir. Eğitim ve test için Xception, VGG16, InceptionV3 ve Resnet50 derin öğrenme ağları kullanılmıştır. Eğitilen ağlar karşılaştırılıp OCT veri seti için ağların performansları değerlendirilmiştir. Derin öğrenme ağlarının test edilmesi sonucunda Resnet50 ağı 0.95, VGG16 ağı 0.95, InceptionV3 ağı 0.97 ve Xception ağı 0.98 doğruluk oranı OCT veri setini sınıflandırmıştır. 0.96 değerindeki F1 skoruyla Xception ağı kullanılan OCT veri seti üzerindeki en başarılı sınıflandırmayı elde etmiştir.

Machine learning is increasingly vital in cybersecurity, especially in malware detection. However, concept drift, where the characteristics of malware change over time, poses a challenge for maintaining the efficacy of these detection systems. Concept drift can occur in two forms: the emergence of entirely new malware families and the evolution of existing ones. This paper proposes an innovative method to address the former, focusing on effectively identifying new malware families. Our approach leverages a supervised autoencoder combined with triplet loss to differentiate between known and new malware families. We create clear and robust clusters that enhance the accuracy and resilience of malware family classification by utilizing this metric learning technique and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The effectiveness of our method is validated using an Android malware dataset and a Windows portable executable (PE) malware dataset, showcasing its capability to sustain model performance within the dynamic landscape of emerging malware threats. Our results demonstrate a significant improvement in detecting new malware families, offering a reliable solution for ongoing cybersecurity challenges.

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment.

Concept drift poses a major challenge for long-lived malware family classifiers. While drift-aware systems have been proposed, most remain opaque, flagging or adapting to drift but rarely explaining why it occurs. This study extends a triplet-autoencoder and DBSCAN-based drift detection framework with explainability and validation components designed to analyze feature- and behavior-level changes underlying drift decisions. Static Windows PE binaries are represented using EMBER features and embedded into a discriminative latent space, where family-specific DBSCAN clusters define decision regions and per-family distance thresholds. We apply Integrated Gradients (IG) to explain drift decisions at the feature level and introduce a counterfactual validation strategy to assess whether these explanations correspond to causally meaningful changes. We define the Drift Correction Rate (DCR) to quantify explanation consistency. IG is compared against the model-agnostic baselines LIME and SHAP. Separately, we conduct an independent behavioral analysis using capabilities extracted by CAPA and their mappings to MITRE ATT& CK techniques to examine whether samples flagged as drifted also exhibit observable behavioral differences. Experiments on a malware dataset spanning multiple Windows families show that the detector flags covariate drift in approximately 30% of post-training samples. IG-based explanations achieve near-perfect drift correction across families and consistently outperform SHAP and LIME. Behavioral similarity metrics based on the Jaccard index and Jensen–Shannon divergence further indicate that drifted samples diverge from the training distribution not only in feature space but also in extracted capabilities and MITRE ATT& CK techniques. Overall, the results show that the proposed framework provides empirically validated feature-level explanations of latent-space drift, while the independent behavioral analysis offers additional evidence that many drifted samples also differ in observable malware capabilities and MITRE ATT& CK techniques.