BEHSOF: Advanced Non-alcoholic fatty liver dataset with clinical metadata and ultrasound images for Deep learning Models

The liver is regarded as one of the essential organs in the body, playing a crucial role in digestion, nutrient absorption, and food processing. This indispensable organ is tasked with cleansing the blood that flows from the digestive tract. Additionally, the liver detoxifies harmful substances and metabolizes various medications. A key function of this organ involves intricate metabolic processes that transform food into energy. However, the liver's heightened sensitivity makes it susceptible to a variety of common ailments, underscoring the need for careful attention to its health.Among liver diseases, fatty liver stands out as the most prevalent, characterized by the buildup of fat within liver cells. This condition is particularly common among individuals who are overweight or have abdominal obesity. Specifically, non-alcoholic fatty liver disease (NAFLD) refers to the excessive fat accumulation in liver cells, a condition known as steatosis. Various diagnostic techniques have been developed for this disease, each offering distinct advantages and drawbacks. Ultrasound imaging has gained popularity due to its accessibility, non-invasive nature, and affordability. One of the approaches utilized in this diagnostic process is the employment of deep learning models. Sporadic studies have been conducted to leverage the available data in diagnosing the degree of non-alcoholic fatty liver disease, and given the significance of the issue, this body of research is continually expanding. Developing robust model training and conducting comprehensive analyses necessitates access to a diverse and comprehensive data repository that can be leveraged for training and validating the proposed models. To this end, we present the BEHSOF dataset, which comprises a collection of samples gathered with varying degrees of non-alcoholic fatty liver disease. Specifically, this data bank consists of ultrasound images from a population of 113 individuals under study, along with the corresponding labels for the levels of steatosis and fibrosis. In addition to the ultrasound images, the data bank provides clinical information, blood test results, and Fibroscan outcomes for the participants, which serve as reference data for fibrosis assessment. Finally, we showcase the results of two deep learning models as examples for training and testing the introduced data set.The ultrasound images have been categorized into two distinct groups based on the diagnostic findings and labeling conventions. Each file within these categories is further distinguished by the location of the medical facility (Taleghani Hospital (TAL) or Behbood Clinic (BEH)), the row number, and the order of grading (steatosis score by expert, steatosis score by CAPscore, fibrosis score by the rate of Elasticity) (TALXXXXX, BEHXXXXX).