BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images
نویسندگان
چکیده
In clinical treatment, deep learning plays a pivotal role in medical image classification. Deep techniques provide opportunities for radiologists and orthopedic to ease out their lives with faster more accurate results. The traditional approach nevertheless reached its performance ceiling. Therefore, this paper, we investigate different enhancement boost the neural networks solution as BoostNet. experiment is categorized into four phases. We have selected ChampNet from benchmark models (EfficientNet: B0, MobileNet, ResNet18, VGG19). This phase helps obtain best model. second phase, evaluates resolution datasets. finalize dataset enhance of ChampNet. third Champ-Net merges techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE), High-frequency filtering (HEF), Unsharp masking (UM). Boost-Net enriched performance. last us verify BoostNet results Lightness Order Error. presented research work fuses technique generate models. An assessment was performed on Musculoskeletal Radiograph Bone Classification using classification schemes demonstrate proposed model's accuracy train test without techniques. model + CLAHE, HEF, UM achieved 95.88%, 94.99%, 94.18% accuracy, respectively. leads efficient main aim paper
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ژورنال
عنوان ژورنال: International Journal of Systems Assurance Engineering and Management
سال: 2022
ISSN: ['0976-4348', '0975-6809']
DOI: https://doi.org/10.1007/s13198-021-01580-3