SMARTORAL: Deep Learning Architecture for Prognostic Mapping of Oral Cancer

Oral cancer is one of the most common and life-threatening cancers worldwide, with survival rates highly dependent on early diagnosis and timely treatment. Traditional diagnostic procedures rely on manual clinical examination and histopathological analysis, which may lead to delayed detection and inconsistent interpretation. Recent advancements in artificial intelligence have enabled automated medical image analysis systems capable of supporting clinicians in disease diagnosis. This paper proposes SMARTORAL, a deep learning architecture designed for prognostic mapping and automated detection of oral cancer using medical images. The proposed framework utilizes convolutional neural networks to extract hierarchical features from oral lesion images and classify them into cancerous and noncancerous categories. The architecture integrates deep feature extraction layers, optimized classification modules, and regularization strategies to enhance model performance and reduce overfitting. Experimental evaluation was conducted on an oral cancer image dataset using performance metrics such as accuracy, precision, recall, and F1-score. The proposed SMARTORAL model achieved superior classification performance compared with conventional machine learning algorithms. The results demonstrate the potential of deep learning-based systems to assist healthcare professionals in early oral cancer diagnosis and clinical decision-making.