Object Detection for Brain Cancer Detection and Localization
Source abstract
Abstract
Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% of patients diagnosed with this malignant cancer do not survive. This paper introduces a method designed to detect and localize brain cancer by proposing an automated approach for the detection and localization of brain cancer. The method utilizes magnetic resonance imaging analysis. By leveraging the information provided by brain medical images, the proposed method aims to enhance the detection and precise localization of brain cancer to improve the prognosis and treatment outcomes for patients. We exploit the YOLO model to automatically detect and localize brain cancer: in the analysis of 300 brain images we obtain a precision of 0.943 and a recall of 0.923 in brain cancer detection while, relating to brain cancer localization, an mAP_0.5 equal to 0.941 is reached, thus showing the effectiveness of the proposed model for brain cancer detection and localization.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Advanced Neural Network Applications · Medical Image Segmentation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Institute for Informatics and Telematics, National Research Council of Italy, 56124 Pisa, Italy
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Funding
MUR-REASONING: foRmal mEthods for computAtional analySis for diagnOsis and progNosis in imagING-PRIN · National Plan for NRRP Complementary Investments D^3 4 Health: Digital Driven Diagnostics, prognostics and therapeutics for sustainable Health care · e-DAI (Digital ecosystem for integrated analysis of heterogeneous health data related to high-impact diseases: innovative model of care and research) · Health Operational Plan
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