RESUMEN
A brain tumor is an abnormal growth of brain cells inside the head, which reduces the patient's survival chance if it is not diagnosed at an earlier stage. Brain tumors vary in size, different in type, irregular in shapes and require distinct therapies for different patients. Manual diagnosis of brain tumors is less efficient, prone to error and time-consuming. Besides, it is a strenuous task, which counts on radiologist experience and proficiency. Therefore, a modern and efficient automated computer-assisted diagnosis (CAD) system is required which may appropriately address the aforementioned problems at high accuracy is presently in need. Aiming to enhance performance and minimise human efforts, in this manuscript, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid over-fitting in the network. Second, the tumor proposals or locations are obtained based on the agglomerative clustering-based method. Third, image proposals and enhanced input image are transferred to backbone architecture for features extraction. Fourth, high-quality image proposals or locations are obtained based on a refinement network, and others are discarded. Next, these refined proposals are aligned to the same size, and finally, transferred to the head network to achieve the desired classification task. The proposed method is a potent tumor grading tool assessed on a publicly available brain tumor dataset. Extensive experiment results show that the proposed method outperformed the existing approaches evaluated on the same dataset and achieved an optimal performance with an overall classification accuracy of 98.04%. Besides, the model yielded the accuracy of 98.17, 98.66, 99.24%, sensitivity (recall) of 96.89, 97.82, 99.24%, and specificity of 98.55, 99.38, 99.25% for Meningioma, Glioma, and Pituitary classes, respectively.
Asunto(s)
Neoplasias Encefálicas , Glioma , Encéfalo/diagnóstico por imagen , Neoplasias Encefálicas/diagnóstico por imagen , Diagnóstico por Computador , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia MagnéticaRESUMEN
OBJECTIVES: The aim of our study is to evaluate anatomic regeneration and metabolic derangement of the liver after major resection in dogs. METHODS: This is an experimental study on 9 dogs; we divided the dogs in two groups: the first group (5 dogs) underwent at one go major hepatectomy (90% of the liver). The second group (4 dogs) underwent successively a resection of 75% of the liver and a second resection of 90% of the restored liver six months later. All dogs underwent a metabolic and morphologic studies of the liver and of their kidney function. RESULTS: In the first group; all dogs which underwent 90% hepatic resection died 48 hours after the surgical resection of hepatic insufficiency. The ultra microscopic study showed the role of portal hypertension in hepatic degeneration on the first group. In the second group, the dogs survived the first resection, and our study shows a regeneration of the liver after resection and sub normal hepatic function. CONCLUSION: The liver is able to regenerate after minimally resection but major resection must be done by successively resection to avoid hepatic dysfunction, but the time between resection must be evaluate later.