Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

To enable researchers to fully trust the decisions made by deep diagnostic models,
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a
driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted …

Early and accurate tomato disease detection using easily available leaf photos is essential
for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics …

Deep Learning (DL) predictions are uncertain; but how uncertain? Statistical inference
estimates the probabilities of uncertainty from a sample drawn from a population. Assessing …

Accurate diagnosis is the key to providing prompt and explicit treatment and disease
management. The recognized biological method for the molecular diagnosis of infectious …

The combination of different sources of information is currently one of the most relevant
aspects in the diagnostic process of several diseases. In the field of neurological disorders …

Degradation under challenging conditions such as rain, haze, and low light not only
diminishes content visibility, but also results in additional degradation side effects, including …

The development of methods based on artificial intelligence for the classification of medical
imaging is widespread. Given the high dimensionality of this type of images, it is imperative …