Lung cancer classification and detection using convolutional neural networks

The immense growth of technology has led to a booming development in the medical science research field. One of the major focuses of researchers is cancer detection in different organs like brain, breast, lung, etc. Lung cancer has a higher cause of death amongst the other cancer types all over the world. Undoubtedly, the most critical point in lung cancer is its early detection where it can lead many patients to survive against the illness. Therefore, one of the most important parts in fighting against lung cancer is detecting it in earlier stages and that's why many systems are being developed with the technology development for achieving this goal. In this work, a recognition system for identifying some lung cancer types including small cell lung cancer, adenocarcinoma, squamous cell cancer, large cell carcinoma, undifferentiated non- small cell lung cancer and also for identifying normal lung is proposed. The proposed algorithm is based on deep learning and convolutional neural network. The system is implemented by transfer learning of MATLAB GUI and it is trained and tested by the data which is collected in K1 hospital located in Kirkuk city, Iraq. The system's convolutional neural network architecture has been developed in deep learning network and it is designed with seven layers and trained in transfer learning with almost 100 samples for each lung cancer type and 50 samples for normal lung. It is found that the proposed system has been successfully worked for the defined purposes.