When employing x-ray images, fracture identification in orthopaedics is a difficult task. A large percentage of humerus fracture patients are seen in hospitals, particularly in their emergency departments. Similar to this, after a fracture, accurate callus production monitoring is crucial for bone healing. Thus, a fractured patient's diagnosis and therapy must be accurate and administered promptly. This work investigates the use of deep learning on X-ray images of the humerus for fracture snd bone callus formation analysis to help physicians in the diagnosis of such fractures, especially in emergency settings.This study is named HD-NET, which stands for Humerus Deep Net. The framework includes image enhancement using a Gaussian filter and histogram equalization, two-stage object detection, image super-resolution, U-NET segmentation with feature recalibration. Finally, an LSTM with a sequence length of 2 is used to analyze callus formation at the fracture site. The LSTM takes the segmented area as input and outputs a prediction for the stage of healing and potential complications. The proposed framework was evaluated on a combination of the MURA dataset and a self-collected dataset.Results demonstrated that in terms of specificity, sensitivity, and accuracy, the suggested framework performed better than earlier studies. This research can be expanded to different bone types and is useful for orthopaedic practitioners.