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Abstract: Cancer, notably brain and lung cancers, is a leading global cause of death, challenging to detect early. Traditional diagnostic methods struggle due to their complexity and lack of specific symptoms. Deep learning models show promise but need improvement, especially for early-stage brain and lung cancers. Challenges include limited data, complex features, and interpretability issues. This research aims to enhance deep learning methodologies by incorporating advanced techniques like transfer learning and attention mechanisms. The goal is to accurately detect and classify early-stage cancers, addressing existing challenges, gaining insights into biological mechanisms, and ultimately improving patient outcomes through earlier detection and treatment.