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Lung cancer is one of the deadliest forms of cancer, witnessing thousands of new diagnoses annually. Early detection remains paramount; without it, survival rates plummet drastically. This underscores the critical role of employing artificial intelligence (AI) for early diagnosis, a pivotal step in combating this devastating illness. This study introduces a sophisticated computer-aided system, aiming to revolutionize lung cancer detection through state-of-the-art convolutional neural network (CNN) technology. By harnessing the capabilities of AI and CNN's, enabling precise categorization of patients into those exhibiting normal lung tissue, benign lung nodules, or malignant lung cancer.The primary objective is to streamline early diagnosis efforts, thereby facilitating prompt intervention and treatment initiation to enhance patient outcomes and bolster survival rates. Leveraging cutting-edge technology, this innovative approach aims to transform the landscape of lung cancer diagnosis, offering hope for more effective strategies in combating this deadly disease. Furthermore, by harnessing the capabilities of AI and CNN technology, this study aims to bridge existing gaps in lung cancer diagnosis, offering new insights and opportunities for advancements in medical research and clinical practice. Ultimately, the successful implementation of this innovative approach has the potential to significantly impact the field of lung cancer diagnosis and treatment, offering hope for improved patient outcomes and increased survival rates. Through continued research and development, further advancements in AI-based diagnostic tools can be achieved, paving the way for a brighter future in the fight against lung cancer.
Awan et al. (2024) studied this question.