A Unified DL Framework for Dual Cancer Diagnosis: Responding to Environmental and Health Challenges

This study proposes an innovative way to resolve the complex difficulty of dual cancer diagnoses influenced by both genetics and environmental factors. Our focus is on lung cancer as well as skin cancer, for the dual cancer analysis. Our methodology improves early and precise cancer diagnosis by leveraging cutting-edge machines and deep learning techniques. We use CNN and EfficientNet to greatly analyze chest X-ray pictures for lung cancer detection in the model. This method facilitates the identification of different lung cancer subtypes. In the instance of skin cancer, we use a multi-modal approach that includes dermatoscopic pictures as well as patient histories. The comprehensive HAM10000 dataset, which includes varied pigmented skin lesions and meticulously annotated labels, is used to investigate skin cancer, which is frequently connected with UV radiation exposure. Early identification of skin cancer is now possible. The biopsy method is the formal method for detecting skin cancer. Our research aims to close the gap between cancer diagnosis and environmental health by providing a complete view of cancer causes. The model’s interpretability makes it well-suited for clinical implementation, giving healthcare practitioners confidence. By adding environmental concerns, our unified DL framework has the potential to change cancer detection, ultimately improving patient care and reducing the burden of late-stage cancer treatments.