چكيده به لاتين
In the early diagnosis of cancer, image processing as a decision-making tool helps physicians detect the disease. Early detection of cancer through image screening is the most important contribution to reducing mortality from a particular cancer. Medical imaging plays an important role in all stages of prognosis, screening, identification, staging, prognosis, treatment planning, response to treatment, recurrence and cancer relief. In this study, in order to improve the accuracy of determining and diagnosing cancerous glands in pulmonary images, a combined method consisting of artificial neural network and water wave algorithm has been used. A total of 140 images were obtained from the TCIA database, from which two normal and cancerous images were selected and classified using the ANN algorithm (specifically the MLP model) after extracting and training the features. In order to increase the accuracy of diagnosing the classification and abnormalities expressing cancerous glands in the lung, the Water Wave Optimization (WWO) algorithm has been used to detect areas related to lung cancer by applying thresholding, segmentation and extraction of features. The results show that by applying the WWO algorithm, the accuracy of the ANN algorithm is 5.86%, its sensitivity is 4.08% and the accuracy of the answers is 4.94%.
