# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 2 Literature Review
### ML-Based Approaches for Animal Classification and Detection  
Nowadays, CNN has become an immensely popular learning method used for image classification problems [12]. There have been several attempts to automatically classify animals based on input images into distinct categories using CNN. [PERSON] and [PERSON] [13] proposed a novel model based on deep convolutional neural networks to detect and categorize two classes of vertebrate animals: mammals and reptiles. Their proposed algorithm was trained on 4000 images and later tested using 1200 images. They achieved an accuracy of 97.5% for their system's prediction of target objects. Additionally, [PERSON] et al. [14] proposed a CNN model to classify input images into categories of fox, wolf, bear, hog, or deer and compared their proposed CNN model with well-known algorithms for image recognition, feature extraction, and image classification, including PCA, LDA, SVM, and LBPH. They obtained the best animal recognition accuracy of 98% using their proposed CNN. Furthermore, [PERSON] et al. [15] developed an algorithm to categorize images of dogs and cats by investigating two approaches to address their targeted problem. Their first approach is a traditional pattern recognition model by which they trained the classification model from some human-crafted features, like color, Dense-SIFT, and a combination of the features. Their second approach uses deep convolutional neural networks to learn the features of images by training neural networks and SVMs for classification. They have conducted different experiments to evaluate the performance of the two approaches on a test dataset. The second approach outperforms the first approach with an accuracy of 94%. Moreover, [PERSON] [16] tried to classify similar animal images by applying a simple CNN. He focused on binary classification for snub-nosed monkeys and normal monkeys which are considered hard to distinguish manually in real-time. He used a database constructed by a Python crawler that consists of 1000 images for each class, split as two subsets of 800 and 200 images, used as the training set and the test set. His model achieved an accuracy of 96% after many attempts of optimization in terms of hyperparameters and model structures. In addition, [PERSON] [17] trained a deep learning model to distinguish between images of cats and dogs. He first trained a VGG model from scratch and then used transfer learning to further improve the accuracy of the model. His objective is to study household animals' demeanor and body language to find out if these animals are sick and offer necessary help to the sick animals in time. Through transfer learning the model's accuracy is increased from 80% to over 95%. Also, [PERSON] and [PERSON] [18] proposed a mammal classification system based on the Inception-v3 CNN architecture to classify mammals into lions, tigers, elephants, giraffes, and monkeys. They utilized transfer learning to fine-tune their model. Their model gained a classification accuracy of approximately 95% which is higher than other methods proposed for the same classification task.