Clasificación de cáncer de pulmón en imágenes de tomografías mediante procesamiento de imágenes y aprendizaje automático

La detección de cáncer de pulmón puede resultar complicada para los profesionales de la salud en sus primeras etapas, ya que es difícil identificarlo a partir de imágenes médicas, lo que supone un obstáculo para comenzar un tratamiento adecuado para los pacientes. Esta enfermedad es la principal causa de muerte, con un incremento de nuevos casos, fallecimientos y cada año mueren más personas por este cáncer que por cáncer de mama, próstata y colon. Las técnicas de clasificación tradicionales tienden a no mejorar sus métricas de evaluación debido a sus procesos de filtrado, segmentación, extracción de características y clasificación. La detección tradicional requiere una gran cantidad de tiempo y recursos económicos. La metodología consta de seis pasos: se inicia con una investigación previa para revisar diferentes estudios. Luego, se selecciona un conjunto de datos. En la tercera etapa se eligen las arquitecturas más destacadas para clasificar con relación al conjunto de datos ImageNet. La cuarta etapa se configuran los modelos para entrenamiento y validación. La quinta etapa se evalúa el consumo de recursos y rendimiento de los modelos. Finalmente, se crea una aplicación web que emplea la arquitectura con los mejores resultados. Después de analizar las arquitecturas seleccionadas se obtuvo métricas porcentuales de 97% o más. Sin embargo, las pruebas revelaron que las métricas de exactitud y precisión alcanzaron porcentajes de 95% y 91%, respectivamente. En conclusión, Efficientb4_DA logra mejores resultados alcanzando una exactitud de 95.32%, una precisión de 91.29%, una sensibilidad de 89.84% y una puntuación F de 90.54%.TesisInfraestructura, Tecnología y Medio Ambient

Automating Lung Cancer Identification in PET/CT Imaging

Early and accurate diagnosis of lung cancer is one of the most investigated open challenges in the last decades. The diagnosis for this cancer type is usually lethal if not detected in early stages. For these reasons it is clear the need of creating an automated diagnostic tool that requires less time for the identification and do not require a cross validation of the results by different radiologists, being in this way cheaper and less error prone. The aim of this work is to implement a completely automated pipeline that starting from the current imaging technologies, such as Computed Tomography (CT) and Positron Emission Tomography (PET), it will identify lung cancers to be employed for the staging; moreover, it will be a suitable starting point for a machine learning based classification procedure. In particular, this project proposes both a methodology and the related software tool that taking as input Digital Imaging and COmmunications in Medicine (DICOM) files of chest PET and CT and by exploiting the characteristic of both of them is capable of automatically identify the lungs and the eventually presence of tumor lesions. Going deeper into the topic and analyzing the literature it is possible to notice as very different solutions have been proposed in literature for an accurate and fast identification of lung cancer. Often such solutions start from CT and PT but generally they are semiautomatic tools that still require the intervention of a physician in charge of indicating which is the Region of Interest (ROI). On the other side, when fully automatic approaches has been proposed, they are closed source with not available datasets, so it is pretty impossible a comparison, as first, and to use their results to move forward machine learning based approaches. For this reason, this work proposes a methodology and its technical implementation that aim at being a reference point for future work into the field. As it will be possible to see, the thesis main contribution is about the proposition of a fully automated identification of the Region of Interest in the medical images, a fully automated segmentation procedure able to find lung cancer lesion inside the ROI and a technique for combine information obtained from both CT and PET. A validation of the pipeline will be also discussed, measuring both the execution time and the obtained accuracy. Moreover, some consideration about future developments of this project will be proposed

Automating Lung Cancer Identification in PET/CT Imaging

reserved5siEarly and accurate diagnosis of lung cancer is one of the most investigated open challenges in the last decades. The diagnosis for this cancer type is usually lethal if not detected in early stages. For these reasons it is clear the need of creating an automated diagnostic tool that requires less time for the identification and does not require a cross-validation of the results by different radiologist, being in this way cheaper and less error prone. The aim of this work is to implement a completely automated pipeline that starting from the current imaging technologies, such as Computed Tomography (CT) and Positron Emission Tomography (PET), will identify lung cancer to be employed for the staging; moreover, it will be a suitable starting point for a machine learning based classification procedure. In particular, this project proposes both a methodology and the related software tool that taking as input Digital Imaging and COmmunications in Medicine (DICOM®) files of chest PET and CT and by exploiting the characteristics of both of them is capable of automatically identify the lungs and the eventually presence of tumor lesions. A validation of the image processing pipeline has been done by computing the execution time and the reached accuracy. The obtained accuracy varies between 89-97% on the analyzed dataset with a significant reduction of the analysis time.mixedD'Arnese E.; Del Sozzo E.; Chiti A.; Berger-Wolf T.; Santambrogio M.D.D'Arnese, E.; Del Sozzo, E.; Chiti, A.; Berger-Wolf, T.; Santambrogio, M. D