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As a result of rising population and pollution, there has been a significant increase incancer-causing substances, necessitating the need to detect cancer at an early stage sothat it can be treated more effectively. The steps to treat or prevent cancer or other tumours are designed to cause little or no death, which is why a system to anticipatethe early stages of cancer and the precautions that will be taken to prevent it fromspreading further is needed. We've chosen to develop a technology that can identifylung cancer in early stage. Lung cancer is divided into phases, and in order to treat it,we must first study each step thoroughly. Lung cancer is divided into four stages. The early stage of cancer is absolutely undetectable, so we must utilize micro scopic detection for a preliminary inspection, which is highly expensive and limited in number. Because these devices have a limited reach in rural and small suburban regions, they must operate under the assumption that we are suffering from cancer andnever truly know if we are.As a result, early detection of cancer makesit easier tocure it, and the patient suffers less as a result of the disease. Because the tumour is small and difficult to detect even at the microscopic level, microscopic analysis of this malignancy some times provides false results. It will be easier to notice such events the next time if we keep track of them, and the chances of making an error will be minimised. This is where our system and technology come into play. It's easier to train the machine for intangible instances of findings that can't be neglected in real life because we're using machine learning to build a computer-aided design system forearly lung cancer diagnosis. Our approach is entirely built on data investigation of various cancer circumstances ranging from early stage to late stage lung cancer. We are using lung cell images taken under FOLDSCOPE.