Accurate food image classification is often critical to accurately monitor the dietary assessment to reduce the risk of different heart-related diseases, obesity, diabetes, and other related health conditions. The accuracy and efficiency of image classification results when using traditional deep learning methods were less than optimal. This research aimed at enhancing the classification and prediction accuracy of food images and reducing the processing time by using the Deep Convolutional Neural Network (DCN) algorithm. The solution starts by using the Modified Loss function, the images are fed into the DCN for features extraction through alternating between convolutional layers and pooling layers, then this is followed by a fully connected layer. Finally, the Softmax function is used to classify the images. The result was compared during the classification phase in the DCN. The proposed solution enhanced the accuracy of the classification by using the regularized loss function and lowered the processing time by decreasing the weights of the neurons in the neural network. Probability score is used as the evaluation metric for the accuracy, and total execution time is used as the evaluation metric for the speed of the algorithm. The combination of deep neural network with regularized cross entropy cost function has improved the fast-food images classification by ahcieving better processing time by 40 ~ 50s and accuracy by 5% in average.