A Multi-featured Approach by Integrating Digital Hand and Dental X-Ray for Human Age Estimation

Traditionally, human bone age is estimated manually by inspecting the multiple body part X-ray images, which is extremely time-consuming and prone to error. The accuracy of the human estimate depends on the experience of the medical practitioner, and thus it suffers from intra- and inter-observer variability. Hence, efficient automatic approaches are required to determine human age with high accuracy. In this work, we propose a human age estimation technique using Deep Learning (DL) technique based on hand X-ray images combined with dental orthopantomographs (OPGs) is proposed. Here, the input X-ray image is pre-processed first using Non-Local Means (NLM) first, followed by Region of Interest (RoI) extraction. Later, color and position image augmentation are performed in order to balance the dataset. Thereafter, the salient features in the image are determined, and based on these features, human age estimation is carried out using the Deep Residual Network (DRN). Here, the DRN is trained using the Beluga whale lion optimization (BWLO) algorithm. Furthermore, the BWLO_DRN is examined for its superiority considering the model accuracy and is found to obtain value of 90.1% on hand-wrist and 89.9% OPG real time dataset, thus showing superior performance for hand-wrist images

Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping

The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step