New bidirectional recurrent neural network optimized by improved Ebola search optimization algorithm for lung cancer diagnosis

The early detection of cancerous and malignant lung cancer by medical imaging techniques, CT-scan for example, which never needs to do sampling reduces the risk of cancer growth and spreading. Accordingly, computer image processing and diagnostic system development, followed by cancer's classification into malig-nant and benign, is of primary importance in the early discovery of lung cancer which plays a pivotal role in the treatment improvement and saving the patient's life. This work intended to improve malignant and benign gland categorization accuracy and, as a result, detection accuracy. Here, a new methodology has been proposed to get an accurate lung cancer diagnosis system using an improved Bidirectional Recurrent neural network. The improvement of the network has been done by designing an improved form of an Ebola optimization search algorithm. Before applying the major diagnosis system, some preprocessing techniques have been done. The model is then applied to IQ-OTH/NCCD lung cancer dataset and its results are compared with some published works to indicate the eminence of the suggested method toward the comparative ones.