Over the past several decades, we have observed three significant technological revolutions in medical imaging, specifically as it relates to AI, the digitization of images in medicine, natural language processing algorithms, and machine learning algorithms that can analyze large-scale radiological data as a result. They allow early detection and diagnosis of diseases like cancer and provide new ground for extending the treatment planning possibilities, with sometimes even better outcomes than conventional radiological images. In this study, a computer-aided detection and classification system of brain tumors, specifically low-grade (LG) and high-grade (HG) gliomas in MRI data is proposed on BraTS-2015 dataset. Different supervised machine learning algorithms and feature extraction methods were performed after the steps of image acquisition, preprocessing, feature selection, and extraction. The models’ performance was tested by different evaluation metrics. The experimental results show that the proposed methods are accurate, fast, and robust, indicating their potential to enhance diagnostic reproducibility and reduce the processing time in brain MRI analysis.
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Intelligent Platform for Medical Diagnosis and Tumor Detection
Semantic Scholar · 2026
Abstract
Over the past several decades, we have observed three significant technological revolutions in medical imaging, specifically as it relates to AI, the digitization of images in medicine, natural language processing algorithms, and machine learning algorithms that can analyze large-scale radiological data as a result. They allow early detection and diagnosis of diseases like cancer and provide new ground for extending the treatment planning possibilities, with sometimes even better outcomes than conventional radiological images. In this study, a computer-aided detection and classification system of brain tumors, specifically low-grade (LG) and high-grade (HG) gliomas in MRI data is proposed on BraTS-2015 dataset. Different supervised machine learning algorithms and feature extraction methods were performed after the steps of image acquisition, preprocessing, feature selection, and extraction. The models’ performance was tested by different evaluation metrics. The experimental results show that the proposed methods are accurate, fast, and robust, indicating their potential to enhance diagnostic reproducibility and reduce the processing time in brain MRI analysis.