Brain tumors are masses or abnormal growths of cells within the brain or the central spinal canal with symptoms such as headaches, seizures, weakness or numbness in the arms or legs, changes in personality or behaviour, nausea, vomiting, vision or hearing problems and dizziness. Conventional diagnosis of brain tumour involves some tests and procedure which may include the consideration of medical history, physical examination, imaging tests (such as CT or MRI scans), and biopsy (removal and examination of a small piece of the tumor tissue). These procedures, while effective, are mentally strenuous and time demanding due to the manual examination of the brain scans and the thorough evaluation of test results. It has been established in lots of medical research that brain tumours diagnosed and treated early generally tends to have a better prognosis. Deep learning techniques have evolved over the years and have demonstrated impressive and faster outcomes in the classification of brain tumours in medical imaging, with very little to no human interference. This study proposes a model for the early detection of brain tumours using a combination of convolutional neural networks (CNNs) and extreme gradient boosting (XGBoost). The proposed model, named C-XGBoost has a lower model complexity compared to purely CNNs, making it easier to train and less prone to overfitting. It is also better able to handle imbalanced and unstructured data, which are common issues in real-world medical image classification tasks. To evaluate the effectiveness of the proposed model, we employed a dataset of brain MRI images with and without tumours.
翻译:大脑肿瘤的常规诊断涉及一些测试和程序,其中可能包括考虑到医学史、物理检查、成像测试(如CT或MRI扫描)和生物检查(摘取和检查一小部分肿瘤组织),这些测试程序虽然有效,但由于对大脑扫描进行人工检查和对测试结果进行彻底评估,在精神上和时间上都非常紧张,而且由于对大脑扫描进行彻底的检查和对测试结果进行彻底评估,因此,这些程序在精神上和时间上都非常紧张。在很多医学研究中,诊断和早期治疗的脑肿瘤往往具有较好的预感性。深层学习技术经过多年的演变,在对医学成像中的脑肿瘤分类(如CT或MRI扫描)和生物检查(摘取和检查一小部分肿瘤组织)。本研究提出了一种早期检测脑肿瘤的模型,这种模型结合了大脑神经网络(CNNs)的人工检查和对测试结果的彻底评估结果的评估。在很多医学研究中,诊断和早期的梯度提升(Xoo)的诊断和早期治疗结果一般的诊断结果往往会比较不那么容易。 提议的深度学习技术技术在医学结构中,因此,称为BISXUB公司的模型可以更难到更容易地进行。