## 2 Materials and Methods
### Composite Dataset Curation  
This study integrated two publicly available retinal OCT datasets to enhance pathological diversity and sample size. The first dataset [39] comprises 24,000 high-resolution JPEG images equally distributed across eight retinal conditions: age-related macular degeneration (AMD), choroidal neovascularization (CNV), central serous retinopathy (CSR), diabetic macular edema (DME), diabetic retinopathy (DR), drusen (DRUSEN), macular hole (MH), and healthy retinas (NORMAL). The second dataset [40] provides 84,495 JPEG images across four classes (CNV, DME, DRUSEN, NORMAL). To establish a unified classification framework while balancing healthy versus pathological representation, the combined dataset was restricted to the four pathological categories common to both sources: choroidal neovascularization (CNV), diabetic macular edema (DME), drusen (DRUSEN), and healthy retinas (NORMAL). This selective curation strategy achieved approximate parity between normal scans (NORMAL class) and aggregated pathological cases (CNV/DME/DRUSEN, classified as \"abnormal\"), with pathological conditions excluded from the first dataset (AMD, CSR, DR, MH) being omitted due to lack of counterpart images in the second source. The resulting binary classification framework preserved clinical relevance through standardized diagnostic groupings while optimizing training conditions through balanced class distributions. Hence, the datasets utilized in this study are publicly available and were not collected by the authors. According to [PERSON] et al. (2018) [40], the larger dataset was acquired using Spectralis OCT equipment (Heidelberg Engineering, Germany) from multiple international centers including institutions in the United States and China between 2013 and 2017. Patient demographics were diverse with no exclusion criteria based on age, gender, or race. All images underwent rigorous quality control and expert labeling through a multitiered grading system involving ophthalmologists and retinal specialists. Similar patient demographic characteristics and image acquisition protocols were maintained in the other dataset [39]. Figure 1 demonstrates the methodology for training and evaluating the ML framework using the above described retinal OCT datasets. The approach merges Dataset 1 (24,000 images spanning six pathologies) and Dataset 2 (84,500 images with three pathologies), retaining only shared diagnostic classes: CNV, DME, DRUSEN, and NORMAL. Images with pathologies exclusive to Dataset 1 (i.e., AMD, CSR, DR, MH) are excluded from training data to create an independent cross-pathology test set. The combined dataset undergoes proportional stratified partitioning into training (70%), validation (15%), and test (15%) subsets, maintaining equivalent distributions of pathological biomarkers across splits. Hence, out of a complete dataset of over 80,000 images, approximately 70% (around 56,000 images) are allocated to the training set, while the remaining 30% are evenly divided between the validation and test sets (about 12,000 images each). This preserves retinal layer deformation patterns and fluid accumulation features in all partitions while preventing data leakage. The validation set guides hyperparameter tuning, while the standard test set evaluates performance on shared pathologies under identical imaging physics domains. The excluded AMD, CSR, DR, and MH cases from Dataset 1 form a dedicated cross-test cohort, enabling evaluation of the model's ability to detect fundamental pathoanatomical disruptions in etiologically distinct conditions. Cross-pathology testing examines whether biomarker representations learned from CNV/DME/DRUSEN generalize to pathologies with analogous structural anomalies but differing clinical etiologies. This strategy mimics real-world diagnostic scenarios where models encounter conditions absent from training repositories, assessing reliance on OCT  
Figure 1: Integration and partitioning methodology for heterogeneous retinal OCT datasets.