Convolutional Neural Network Based Image Classification
Keywords:CK , FER, Emotion Recognition, CNN models, AlexNet, Res-Net-50, SARS-CoV-2 dataset, Grad-CAM, SVM, Fuzzy metric space, Limit theorems, Cauchy sequence, fuzzy diameter, strong and weak convergence, Random set, Fuzzy random variable., Analytic Hierarchy Process
This paper presents the study of Convolutional Neural Network based Image Classification. In this study, different convolutional neural network model is implemented for image classification. There are two application which is performed in this namely, emotion detection from facial emotion recognition system and covid-19 detection from lung CT-scan. In this research work, AlexNet and ResNet-50 convolutional neural network models are used for comparison and evaluation is done based on its training accuracy and testing accuracy, confusion matrix and Area under the curve (AUC) of ROC graph. All the experiments are performed on MATLAB software. In the First application of CNN model is implemented to detect emotions from facial expressions and SVM classifier is used for classification of each emotion among eight facial expressions namely, surprise, contempt, happiness, sadness, fear, anger, disgust, and neutral. The following proposed work is carried out on CK+ datasets to determine the recognition. After performing on both CNN models accuracy of both the models are compared. The result shows that ResNet-50 model achieved the best accuracy of 97.32% which is better than 90.55% which got on AlexNet model. In Second application of CNN model is to detect covid-19 from lung CT-scans. The highest performing model, the ResNet-50 on a SARS-CoV-2 CT-scan dataset achieved an accuracy of 95.72% which is maximum than 85.50% which achieved on AlexNet Model. Gradient-Weighted Class Activation Mapping (Grad-CAM) is also used to display infected area in the lungs. After performing this experiment, the final results shows that the ResNet-50 model, performs much better as compared to AlexNet model.
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