A multi-scale context-aware deep learning framework for medical disease diagnosis

Early and accurate diagnosis of brain tumors, skin lesions, and diabetic retinopathy is essential for effective treatment planning and improving patient prognosis. Nevertheless, the diagnostic process for these conditions remains challenging due to the reliance on conventional methods, which are often subjective, time-intensive, and dependent on expert interpretation. In this context, deep learning-based approaches offer promising opportunities to enhance diagnostic precision and support clinical decision-making. This study introduces BSCRADNet, a novel deep learning model designed for the automated diagnosis of oncological and retinopathic diseases. The proposed architecture employs a multi-layered analytical framework that integrates local and spatial features with long-range contextual dependencies, enabling the effective recognition of complex morphological patterns in medical images. By enhancing diagnostic consistency, BSCRADNet reduces the risk of misclassification—particularly in cases characterized by high inter-observer variability or subtle lesion presentation. The model demonstrated strong performance across multiple datasets, achieving classification accuracies of 94.67% for brain tumors, 89.58% for skin cancer, and 90.40% for diabetic retinopathy. Furthermore, to improve the discriminative power of feature representations across diverse disease categories, a composite hybrid architecture combining BSCRADNet with ResMLP was developed. This hybrid model yielded competitive results, with accuracies of 93.33%, 88.19%, and 87.40% for the respective diagnostic tasks. Experimental findings indicate that the proposed approach is capable of distinguishing intricate patterns in medical imaging data and holds significant potential for integration into clinical diagnostic workflows, contributing to more reliable and efficient disease classification.