Enhance-Net: An Approach to Boost the Performance of Deep Learning Model Based on Real-Time Medical Images

ABSTRACT

Real-time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance-Net. It is possible to classify the experiment into six distinct stages. Champion-Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet B0, MobileNet, ResNet-18, and VGG-19). This stage helps choose the optimal model. In the second step, Champion-Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion-Net performance. The next stage extracts green channel data. In the fourth step, Champion-Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance-performance. The next stage compares the Enhance-Net findings to the lightness order error (LoE). In Enhance-Net models, the current study combines image enhancement and green channel with Champion-Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real-time medical image prediction. The study effort uses the musculoskeletal radiograph-bone classification (MURA-BC) dataset. Classification accuracy of Enhance-Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real-time testing with the unseen dataset.