Deep Belief Networks for Ovarian Tumour Detection From Gene Data

Ovarian cancer is one of the most lethal gynecological malignancies, with early and accurate diagnosis playing a critical role in improving patient survival rates. Traditional diagnostic techniques, such as imaging and biomarker analysis, have limitations in terms of specificity and sensitivity. This study uses a Deep Belief Network (DBN) to classify ovarian tumours based on microarray gene expression data. The dataset consists of gene expression profiles with over 15,000 features, representing continuous values corresponding to molecular markers of cancerous and normal ovarian tissues. The proposed DBN model is trained to extract deep hierarchical features, enabling the robust classification of tumour samples. Performance evaluation uses standard metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the DBN-based approach achieves superior classification accuracy compared to conventional machine learning models. This research highlights the potential of deep learning in analyzing high-dimensional biological data, offering a promising direction for computational oncology and precision medicine.