Alcoholism via 6-layer customized deep convolution neural network

Abstract

Background: Alcoholism is caused by excessive alcohol into the human body. Alcohol primarily damages the central nervous system of the human body and causes the nervous system function disorder and inhibition. Severe addiction can lead to respiratory circulation center inhibition, paralysis and even death. So far, the diagnosis of alcoholism is done by radiologist's manual CT examination. However, the diagnosis process is time-consuming, subjective and boring for doctors. External factors, such as extreme fatigue, lack of sleep and mental concentration, can easily affect the diagnosis process.Methods: In order to solve this problem, this paper proposed a new neural network based on computer vision, which used deep convolution neural network to diagnose alcoholism automatically. A total of 216 brain images were collected. In the 6-layer customized deep convolution neural network structure, there were four convolution layers and two fully connected layers, and each convolution layer was connected with a pooling layer.Results: The results showed that the accuracy, sensitivity, specificity, precision, F1, MCC and FMI were 95.96%±1.44%, 95.96%±1.66%, 95.95%±1.67%, 95.73%±1.72%, 95.84%±1.48%, 91.92%±2.87% and 95.84%±1.48% respectively.Conclusion: It can be concluded from comparison results that the proposed neural network structure is more effective than four state-of-the-art approaches. The proposed method has high accuracy and can be used as a diagnostic method for alcoholism.

Deep Learning in the Classification of Stage of Liver Fibrosis in Chronic Hepatitis B with Magnetic Resonance ADC Images

“…This paper mainly proposed a 5-layer deep convolution neural network structure for the automatic classification of liver fibrosis in chronic hepatitis B. Since the neural network was proposed, it has been optimized and deepened by researchers [ 5 ]. In the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), AlexNet [ 15 ] won the championship.…”

“…Doctors are likely to be interfered with by external factors, such as fatigue, lack of sleep, and so on. With the continuous development of artificial intelligence and computer vision, computer technology has been applied to various fields, such as the analysis of medical images [ 5 ]. Subramaniam et al [ 6 ] used CNN to segment and diagnose medical images.…”