Chandrasekar Thaventhiran, K. R. Sekar^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 495-506, 2022, DOI:10.32604/iasc.2022.019546

Abstract Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANN-LSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple layers to learn the given… More >

Yasemin Gültepe^*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631

Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More >