Arun Kumar Rajasekaran

arXiv2026

Shifting landscape of disability and development in India: Analysis from historical trends to future predictions 2001-2031

This study delves into the causes and trends of disability-related health burdens across Indian states. Through multiple Disability-Adjusted Life Years (DALY) types (covering communicable diseases, noncommunicable diseases, and injuries), gender disparities, and Human Development Index (HDI) values, these disability trends were evaluated. The data for this study was compiled from censuses, health research organisations, and data centres, among various other sources. We built regression models and used them to analyze trends across past decades and make projections for 2031. Our regression results show a strong inverse relationship between communicable disease DALYs and HDI. In other words, ongoing improvements in development and infrastructure significantly reduced communicable disease DALYs. In contrast, noncommunicable DALYs did not decrease despite rising HDI. And lastly, injury DALYs showed moderate declines with higher HDI, which reflects improvements in healthcare and safety systems. Gender analysis showed male overrepresentation among people with disabilities. These results from our study support that there is a need to shift public health focus toward chronic diseases and address gender disparities in disability outcomes.

cs.SI↗

arXiv2025

A Multi-Task Deep Learning Framework for Skin Lesion Classification, ABCDE Feature Quantification, and Evolution Simulation

Early detection of melanoma has grown to be essential because it significantly improves survival rates, but automated analysis of skin lesions still remains challenging. ABCDE, which stands for Asymmetry, Border irregularity, Color variation, Diameter, and Evolving, is a well-known classification method for skin lesions, but most deep learning mechanisms treat it as a black box, as most of the human interpretable features are not explained. In this work, we propose a deep learning framework that both classifies skin lesions into categories and also quantifies scores for each ABCD feature. It simulates the evolution of these features over time in order to represent the E aspect, opening more windows for future exploration. The A, B, C, and D values are quantified particularly within this work. Moreover, this framework also visualizes ABCD feature trajectories in latent space as skin lesions evolve from benign nevuses to malignant melanoma. The experiments are conducted using the HAM10000 dataset that contains around ten thousand images of skin lesions of varying stages. In summary, the classification worked with an accuracy of around 89 percent, with melanoma AUC being 0.96, while the feature evaluation performed well in predicting asymmetry, color variation, and diameter, though border irregularity remains more difficult to model. Overall, this work provides a deep learning framework that will allow doctors to link ML diagnoses to clinically relevant criteria, thus improving our understanding of skin cancer progression.

cs.CV↗