Lung Cell Cancer Identification Mechanism Using Deep Learning Approach

Nowadays, healthcare solutions have accomplished radical advancements in developing diagnostic mechanisms using machine and deep learning (DL) based techniques. The symptoms of lung cell cancer are common to many other ailments; hence the healthcare practitioners may make mistakes in identification of lung cancer in early stages. Cancer enters into human body silently and the symptoms such as weakness, weight loss and fever are common to many other ailments which may confuse the physicians to distinguish between cancer symptoms and other ailment symptoms. Cancer is usually detected in lateral stages when it is difficult to control the further spread of cancer in the other parts of the body. Cancer is one of the major causes of the deaths in the youngsters these days. There are many existing techniques which can help in diagnosis of cancer but still there is a need to explore more intelligent mechanisms which not only identify the presence of cancer in the cells but also determine the stages of the cancer for timely treatment. Hence the proposed DL based diagnostic mechanism not only identifies the presence of cancer in lung cells but also determines the respective stage. A DL-based Lung Cell Cancer Detection (DL-LCCD) is suggested in this paper to detect lung cell cancer. The proposed DL-LCCD method determines the cancerous cells with the aid of digital image processing techniques with high accuracy and precision. A Hybrid CNN model is devised to determine the cancer from the CT scanned images. The evaluation metrics are used to test the viability of the proposed DL-LCCD method. The classification accuracy of 95.30% is achieved with 10-fold cross-validation and 96.10% accuracy with 15-fold cross-validation.