Bone Age and Sex Prediction by Left-hand X-ray Images Dataset using ResNet۵۰ Deep Neural Network

Abstract: Bone age and sex prediction is a crucial diagnostic tool in the assessment of bone developmentand the determination of endocrine and metabolic disorders. In this paper, we presenta novel deep learning approach for bone age and sex prediction using the left-hand X-ray imagesdataset. We utilized a pre-trained ResNet۵۰ deep neural network to extract the features fromthe left-hand X-ray images and perform bone age and sex prediction. The proposed approachachieved high accuracy and outperformed other state-of-the-art methods. ResNet, also known asResidual Network, is a popular deep neural network architecture that was introduced by ShaoqingRen, Kaiming He, Jian Sun, and Xiangyu Zhang in ۲۰۱۵. ResNet has been one of the mostsuccessful deep learning models to date, winning the ILSVRC challenge in ۲۰۱۵. This model’ssuccess lies in its ability to train very deep neural networks with more than ۱۵۰ layers, which waspreviously challenging due to the vanishing gradient problem.The vanishing gradient problem occurs when gradients become too small during backpropagation,making it challenging to update the weights in the network’s early layers effectively. ResNetaddresses this issue by introducing skip connections that enable the flow of information from onelayer to the next without being transformed, effectively allowing the network to learn the identityfunction.Bone age and sex prediction using left-hand X-ray images dataset is a task that requires the abilityto process and analyze large amounts of image data. In this paper, we propose to use the ResNet۵۰deep neural network architecture for this task. ResNet۵۰ is a variant of ResNet that has ۵۰ layersand is capable of achieving high accuracy in image classification tasks.To train and evaluate the proposed model, we will use a publicly available dataset of left-handX-ray images for bone age and sex prediction. The dataset contains a large number of images andannotations, making it suitable for training deep neural networks.Our approach will involve training the ResNet۵۰ model using the dataset and evaluating its performanceon a separate test set. We will also explore techniques such as data augmentation, regularization,and fine-tuning to improve the model’s performance.The results of our experiments will demonstrate the effectiveness of using ResNet۵۰ for bone ageand sex prediction from left-hand X-ray images. This approach has the potential to improve theaccuracy and efficiency of this important medical task, leading to better diagnosis and treatmentfor patients.