علي عموقلي ميراخوري

In this thesis, advanced machine learning techniques, an‎d in particular convolutional neural networks (CNN), have been used to diagnose esophageal cancer. These methods have attracted the attention of many researchers due to their high ability to recognize patterns in medical images. They are attracted to themselves. In this study, a deep model has been designed an‎d developed for the diagnosis of esophageal cancer, which is capable of providing useful information for more accurate diagnosis by accurately analyzing image datasets containing samples related to esophageal cancer. One of the main goals of this research is to provide an effective method for the early diagnosis of esophageal cancer, as rapid diagnosis of this disease can have a significant impact on reducing mortality rates an‎d improving the treatment process. By using deep learning models an‎d image data processing, it is hoped that new tools can be provided that increase the accuracy an‎d reliability of diagnosis compared to traditional methods. In this study, convolutional neural network algorithms have been trained with diverse datasets to be able to identify complex features associated with esophageal cancer. An attempt has also been made to optimize the developed model in such a way that, in addition to high accuracy, it has an acceptable processing speed for use in clinical environments. The results of this research could provide innovative solutions for the timely diagnosis an‎d treatment of esophageal cancer, an‎d also serve as a basis for future research on the use of artificial intelligence in the medical field.

سرطان مري , يادگيري عميق , شبكه عصبي كانولوشني , سرطان , تشخيص تصويري

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Ali Amoogoli Mirakhori

Javad Vahidi