A MI and DenseNet Based Feature Selection and Classification of Gene Expression Data

Introducing a groundbreaking approach, the paper unveils MI_DenseNet, a cancer classification procedure that employs mutual information and deep learning to achieve precise classification. The method involves preprocessing the data, converting it into 2D images, and utilizing a DenseNet model for classification. The experimental results demonstrate that MI_DenseNet outperforms another type of classification methods, showcasing best result. This study dives deep into the world of cancer analysis, where researchers mine data across different cancer types to unlock secrets hidden within. It emphasizes the crucial role of accurate early-stage cancer detection and prediction, not just for individual patients but for shaping the future of cancer research.