A Systematic Study on Enhanced Deep Learning Based Methodologies for Detection and Classification of Early Stage Cancers

Cancer is considered as a principal basis of death worldwide, with brain and lung cancers being particularly challenging to detect and classify in their early stages. Early detection and classification of cancer are crucial for effective treatment and improved patient outcomes. However, traditional diagnostic methods for these cancers often fail to detect them in their early stages due to their complex nature and lack of specific symptoms. Deep learning-based methodologies have shown great promise in the detection and classification of various diseases, including cancers. However, the accuracy of existing deep learning models for early-stage cancer detection and classification still needs improvement, particularly for cancers such as brain and lung cancers. There are several challenges associated with developing accurate methodologies. First, the available data for early-stage cancers is often limited, making it challenging to develop accurate models. Second, these cancers can have complex and heterogenous features, making it difficult to develop models that can accurately detect and classify them. Third, the interpretability of the features learned by deep learning models is limited, making it challenging to gain insights into the biological mechanisms underlying early-stage cancer development. Therefore, the problem statement for this research is to develop enhanced deep learning-based methodologies that can accurately detect and classify early-stage cancers, particularly brain and lung cancers, by addressing the challenges associated with existing methods. The proposed research will incorporate advanced techniques such as transfer learning, attention mechanisms, and adversarial training to progress the accuracy of cancer detection and categorization. Furthermore, the research will investigate the interpretability of the features learned by the proposed models to gain insights into the biological mechanisms underlying early-stage cancer development. Ultimately, the proposed research aims to improve patient outcomes by enabling earlier detection and treatment of brain and lung cancers, which can significantly improve survival rates and quality of life.