This study presents an AI-driven multi-omics framework designed to improve the classification of kidney tumor subtypes and support personalized treatment strategies in precision oncology. By integrating genomic, transcriptomic, and proteomic data from publicly available TCGA repositories, the proposed model builds comprehensive molecular profiles of kidney tumors. The system achieves a classification accuracy exceeding 96% by combining machine learning and deep learning techniques — specifically Random Forest (RF) for feature selection, Support Vector Machines (SVM) for handling high-dimensional data, Convolutional Neural Networks (CNNs) for spatial pattern extraction, and Transformer models to capture contextual relationships across biologically ordered gene sequences. Unlike conventional ensemble approaches, this hybrid framework is optimized for both predictive accuracy and computational efficiency, making it suitable for real-time clinical use. Additionally, the model identifies key tumor-specific biomarkers that can guide individualized therapy. Experimental results confirm that the proposed system significantly outperforms traditional diagnostic methods and single-omics models. Designed for integration with cloud-based Clinical Decision Support Systems (CDSS), the framework has strong potential to enhance real-time oncology decision-making.
R. K, V. Kumari, J.Ruby Elizabeth et al.· 2026 4th International Confe...· 0 citations
Chronic disease early and accurate detection is a major healthcare issue nowadays, and most importantly, there is the rising prevalence or use of heterogeneous medical image data such as CT, MRI, X-ray, and retinal scans. Conventional models of deep learning such as CNNs perform well on spatial aspects of feature extraction but not generally on long-term relations and overall context. In this paper, we introduce a new hybrid deep learning network combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to carry out multi-modal medical image fusion to assist in the diagnostic process with better results. The CNN branch gives fine-grain local representation and the Transformer module will predict the global connections between modalities. Empirical tests on publicly available data show that the proposed model is better than single CNN and Transformer models in using the datasets and there are massive increments in precise score, recall, and F1-score on chronic disease diagnosis in the initial stages. The field of research shows how hybrid architecture can be used to combine complementary information about various scanning modalities, which can become the direction of AI-aided decision-making in prevention.
N.R Azhakeshwari, S. Christy, S. Saranya et al.· 2026 11th International Conf...· 0 citations