Aim: Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.
Methods: We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image). In addition, two networks (Net-I and Net-II) are proposed in ablation studies. Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling. Net-II removes the 20-way data augmentation.
Results: The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32, a specificity of 97.80±1.35, a precision of 97.78±1.35, an accuracy of 97.89±1.11, an F1 score of 97.87±1.12, an MCC of 95.79±2.22, an FMI of 97.88±1.12, and an AUC of 0.9849, respectively.
Conclusion: The performance of our VISPNN model is better than two internal networks (Net-I and Net-II) and ten state-of-the-art alcoholism recognition methods.
Keywords: VGG; alcoholism; convolutional neural network; deep learning; multiple-way data augmentation; stochastic pooling.