Classification and identification of eye diseases using Optical Coherence Tomography (OCT) has been a challenging task and a trending research area in recent years. Accurate classification and detection of different diseases are crucial for effective care management and improving vision outcomes. Current detection methods fall into two main categories: traditional methods and deep learning-based approaches. Traditional approaches rely on machine learning for feature extraction, while deep learning methods utilize data-driven classification model training. In recent years, Deep Learning (DL) and Machine Learning (ML) algorithms have become essential tools, particularly in medical image classification, and are widely used to classify and identify various diseases. However, due to the high spatial similarities in OCT images, accurate classification remains a challenging task. In this paper, we introduce a novel model called “OCTNet” that integrates a deep learning model combining InceptionV3 with a modified multi-scale attention-based spatial attention block to enhance model performance. OCTNet employs an InceptionV3 backbone with a fusion of dual attention modules to construct the proposed architecture. The InceptionV3 model generates rich features from images, capturing both local and global aspects, which are then enhanced by utilizing the modified multi-scale spatial attention block, resulting in a significantly improved feature map. To evaluate the model’s performance, we utilized two state-of-the-art (SOTA) datasets that include images of normal cases, Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). Through experimentation and simulation, the proposed OCTNet improves the classification accuracy of the InceptionV3 model by 1.3%, yielding higher accuracy than other SOTA models. We also performed an ablation study to demonstrate the effectiveness of the proposed method. The model achieved an overall average accuracy of 99.50% and 99.65% with two different OCT datasets.

Cataracts are a leading cause of blindness in Pakistan, contributing to more than 54% of blindness cases in Pakistan, primarily due to poor living conditions, nutritional deficiencies, and limited healthcare access. Early detection is critical to avoid invasive treatments, but current diagnostic approaches often identify cataracts at advanced stages. This paper presents an advanced,automated cataract detection system using deep learning specifically the ResNet-50 architecture, to address this gap. The model processes fundus retinal images curated from diverse datasets, classified by ophthalmologic experts through a rigorous three-stage process. By leveraging the ResNet-50 model, cataracts are categorized into normal,moderate,and severe, achieving an accuracy of 97.15% on full images. Notably, the system performs well even on partial images with 70% visibility, maintaining an accuracy of 95.23%, thus minimizing the need for extensive images restoration. The dataset was augmented to include 17,500 images,ensuring robust training. The model's ability to detect cataracts with high precision in images with varying visibility(70%, 80%, 85% and beyond) demonstrate its flexibility and reliability, consistently achieving accuracy above 95.50%. This research offers a non-invasive, efficient solution particularly suited for remote areas, addressing the limitations of the late-stage diagnoses. It represents a significant advancement in cataract detection and has the potential to revolutionize global cataracts identification through early, accurate intervention.

Intelligent project management systems rely on high-quality historical data for accurate automated decision-making, yet missing data in software project repositories remains a persistent challenge that degrades intelligent estimation performance. This study proposes an Intelligent Decision Support Framework (IDSF) for software development effort estimation (SDEE) that integrates Multiple Imputation (MI) as a critical data quality enhancement layer within the Analogy-Based Effort Estimation (ABEE) model. The framework is evaluated on the ISBSG dataset by systematically comparing six imputation strategies. Results demonstrate that the MI-enhanced framework achieves competitive and more stable MMRE values while fully preserving dataset integrity, in contrast to traditional deletion methods that cause substantial data loss. Additionally, a theoretical analysis of Long Short-Term Memory (LSTM) networks is provided as a prospective deep learning estimator, highlighting that high-quality restored data is structurally necessary for effective LSTM training. This work contributes to intelligent systems in software engineering by establishing MI as a robust data quality module, laying a strong foundation for building more reliable AI-driven intelligent project management systems and advancing intelligent systematics research.