Medical application research based on deep learning-cancer cell classification and recognition

This study aims to combine Raman spectroscopy technology with deep learning algorithms to achieve precise classification and identification of cancer cells. Firstly, a large-scale spectral database is established using datasets. Subsequently, deep learning techniques are employed to design and train various deep learning network models for cancer cell classification. The models can extract high-order features from complex spectral data, enabling accurate identification of different types of cancer cells. Through validation and optimization of the models, their accuracy and reliability in cancer diagnosis will be verified, providing clinicians with an effective auxiliary diagnostic tool that is expected to play a significant role in improving the accuracy of cancer diagnosis and treatment efficacy.