Genome Sequence Identification using Deep Learning for Lung Cancer Diagnosis

Lung cancer will account for around 2,206,771 cases of cancer in human worldwide in 2020, according to data from the International Agency of Research on Cancer. To reduce morbidity and mortality rates, a quick and accurate diagnosis of the illness is crucial. Needle aspiration biopsy, thoracentesis, thoracoscopy, bronchoscopy, mediastinoscopy, thoracotomy, and cancer biomarker testing are some of the frequently used diagnostic techniques. But, with advances in deep learning and the use of a wealth of biological data, a more effective forecast method can be developed. This paper proposes a genome sequence identification using deep learning technique for lung cancer diagnosis.