Ricerc@Sapienza

In recent years, computer-assisted diagnostic systems have gained increasing interest through the use of deep learning techniques. In this work we show how it is possible to classify X-ray images through a multi-input convolutional neural network. The use of clinical information together with the images allowed to obtain better results than those present in the literature on the same data.

In the last few years, computer-assisted diagnosis systems have obtained a growing interest from researchers thanks to the use of deep learning techniques. We propose a deep neural network based on a multi-input architecture that allows to use all the information available to physicians during the diagnosis. The results obtained show an interesting improvement in performance in terms of predictive skill compared to the results in the literature.

In recent years, computer-assisted diagnostic systems increasingly gained
interest through the use of deep learning techniques. Surely, the medical field could
be one of the best environments in which the power of the AI algorithms can be
tangible for everyone. Deep learning models can be useful to help radiologists elaborate
fast and even more accurate diagnosis or accelerate the triage systems in hospitals.
However, differently from other fields of works, the collaboration and co-work

Convolutional Neural Networks (CNNs) have been widely used in the field of audio recognition and classification, since they often provide positive results. Motivated by the success of this kind of approach and the lack of practical methodologies for the monitoring of construction sites by using audio data, we developed an application for the classification of different types and brands of construction vehicles and tools, which operates on the emitted audio through a stack of convolutional layers.