Enhancing Lung Cancer Classification and Prediction With Deep Learning and Multi-Omics Data

Enhancing Lung Cancer Classification and Prediction With Deep Learning and Multi-Omics Data

Lung cancer remains the deadliest cancer worldwide, largely because it is diagnosed at advanced stages. This project addresses the early diagnosis challenge by fusing medical imaging and multi-omics data — genomic, transcriptomic, and proteomic signals — within a unified deep learning architecture. The system employs convolutional neural networks (CNNs) to extract radiological features from CT imaging while deep embedding layers encode omics profiles; a fusion network then combines both modalities to classify tumor type, stage, and malignancy risk with superior accuracy over single-modality baselines. Attention-based interpretability modules highlight which genes and imaging regions influenced each prediction, supporting clinical trust. Developed in Python with TensorFlow, PyTorch, and standard bioinformatics pipelines, this project is a cutting-edge deep learning medical imaging project suited for biomedical engineering students, computational biology researchers, and cancer diagnostics startups exploring multi-omics data analysis, AI cancer detection systems, and precision oncology solutions.

Components

Python 3.8+
TensorFlow & PyTorch
CNN imaging models
Deep embedding layers for omics profiles
Fusion network architecture
Bioinformatics pipelines

Key Features

• CNN extraction of radiological features from CT imaging
• Genomic, transcriptomic and proteomic (multi-omics) data fusion
• Tumor type, stage and malignancy risk classification
• Attention-based interpretability highlighting influential genes and image regions
• Superior accuracy over single-modality baselines on standard benchmarks

Applications

Biomedical engineering students, computational biology researchers and cancer diagnostics startups working on precision oncology and AI cancer detection.