Detección y clasificación de tumores cerebrales mediante aprendizaje profundo y detección compresiva en imágenes de resonancia magnética
Palabras clave:
Sensación compresiva, aprendizaje profundo, imágenes de resonancia magnética, clasificación de imágenes, red neuronal convolucional, tumores cerebralesResumen
En este artículo presenta una nueva metodología para la detección de tumores cerebrales en imágenes de resonancia magnética. Se utilizó la base de datos FIGSHARE de la Universidad de Medicina del Sur, Guangzhou, China. Se utilizaron técnicas de morfología matemática para el acondicionamiento de la imagen con el fin de detectar el área de interés en la imagen. Además, se desarrolló un algoritmo para determinar el tipo de corte (axial, sagital o coronal). Para este procedimiento se utilizó la técnica matemática estadística k-means. Asimismo, se realizó una extracción de patrones a partir de cada imagen utilizando la técnica de detección compresiva (CS). Para la detección, segmentación y clasificación de los tumores se implementó el aprendizaje profundo basado en redes neuronales convolucionales aplicando R-CNN (regiones con redes convolucionales). Los resultados obtenidos en la etapa de validación lograron una clasificación y detección de tumores cerebrales en imágenes de resonancia magnética con altos porcentajes de precisión del 87,5% y 95,2%. Los datos se dividieron en un 65% para el entrenamiento de la red, un 18% para la prueba y un 17% para el proceso de validación. Finalmente, aplicando deep learning y compressive sensing, se detectaron y clasificaron los tumores cerebrales en tres tipos diferentes: meningioma, glioma y pituitaria con un porcentaje de precisión del 87,5% y 95,2%.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
