Aplicación basada en software para detectar Glioblastoma a partir de imágenes de resonancia magnética en formato DICOM utilizando un modelo de red neuronal de aprendizaje profundo.

Abstract

In the current context of technological advances, early and accurate diagnosis of diseases complex such as glioblastoma multiforme (GBM), an aggressive type of brain tumor is essential to improve treatment opportunities and patient survival. The proliferation of digital medical imaging, especially magnetic resonance imaging (MRI) in DICOM format, offers a rich source of data for the identification and analysis of these pathologies.The problem addressed in this work is the need for automated and precise methods for the Detection of glioblastomas on magnetic resonance images. Traditionally, the analysis of These images have depended on manual interpretation by radiologists, which can be a subjective, slow and error-prone process. The main objective of the project is to develop a support system that uses deep neural networks to automatically analyze and detect the distinctive features of glioblastoma on DICOM images.The proposed solution consists of an application with a graphical interface developed in Tkinter, which allows users to upload and view DICOM images, perform preprocessing necessary, and apply a trained convolutional neural network model to identify and highlight suspicious areas of tumor. The implementation of this tool not only seeks to increase the accuracy in detecting glioblastoma, but also speed up the diagnostic process and reduce the workload of medical professionals. The results obtained so far have shown that the system is capable of identifying characteristics of glioblastoma with a degree of accuracy of 91.84% and a precision of 87.5% in reference to patients with glioblastoma tumor on T2 MRIs taken in axial plane. During testing, the neural network model generated accurate predictions in comparison with diagnoses previously made by experts. These findings suggest that The app has the potential to be a valuable support tool in clinical settings. In the current context of technological advances, early and accurate diagnosis of diseases complex such as glioblastoma multiforme (GBM), an aggressive type of brain tumor is essential to improve treatment opportunities and patient survival. The proliferation of digital medical imaging, especially magnetic resonance imaging (MRI) in DICOM format, offers a rich source of data for the identification and analysis of these pathologies.The problem addressed in this work is the need for automated and precise methods for the Detection of glioblastomas on magnetic resonance images. Traditionally, the analysis of These images have depended on manual interpretation by radiologists, which can be a subjective, slow and error-prone process. The main objective of the project is to develop a support system that uses deep neural networks to automatically analyze and detect the distinctive features of glioblastoma on DICOM images. The proposed solution consists of an application with a graphical interface developed in Tkinter, which allows users to upload and view DICOM images, perform preprocessing necessary, and apply a trained convolutional neural network model to identify and highlight suspicious areas of tumor. The implementation of this tool not only seeks to increase the accuracy in detecting glioblastoma, but also speed up the diagnostic process and reduce the workload of medical professionals.The results obtained so far have shown that the system is capable of identifying characteristics of glioblastoma with a degree of accuracy of 91.84% and a precision of 87.5% in reference to patients with glioblastoma tumor on T2 MRIs taken in axial plane. During testing, the neural network model generated accurate predictions in comparison with diagnoses previously made by experts. These findings suggest that The app has the potential to be a valuable support tool in clinical settings.