2024
DOI: 10.1038/s41598-024-54864-6
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A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Naira Elazab,
Wael A. Gab-Allah,
Mohammed Elmogy

Abstract: Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50.… Show more

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Cited by 7 publications
(2 citation statements)
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“…Recent advancements in AI and deep learning have revolutionized medical image analysis, particularly in the detection, segmentation, and classification of tumor tissues in histological images [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Numerous studies have highlighted the efficacy of deep learning models in extracting critical information from routine pathological images, offering valuable clinical insights [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, deep learning has been utilized for quantitative image analysis to forecast disease progression patterns, prognoses, and other clinical outcomes [ 27 , 28 , 29 , 30 , 31 ].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Recent advancements in AI and deep learning have revolutionized medical image analysis, particularly in the detection, segmentation, and classification of tumor tissues in histological images [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Numerous studies have highlighted the efficacy of deep learning models in extracting critical information from routine pathological images, offering valuable clinical insights [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, deep learning has been utilized for quantitative image analysis to forecast disease progression patterns, prognoses, and other clinical outcomes [ 27 , 28 , 29 , 30 , 31 ].…”
Section: Introductionmentioning
confidence: 99%
“…For example, recent studies have demonstrated the application of deep learning models to various medical imaging tasks, achieving high performance metrics. Elazab et al used a combination of YOLOv5 and ResNet50 for brain tumor detection and classification [ 26 ]. Tsuneki et al used the EfficientNetB1 model in their study on multi-organ adenocarcinoma classification [ 21 ].…”
Section: Introductionmentioning
confidence: 99%