BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images

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