# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 2 Literature Review  
The usage of deep learning techniques for medical image classification is a sore subject right now because of the need to assist pathologists and give them a second opinion. [PERSON] and [PERSON] [17] investigated the effect of CNN transfer learning on the performance of three CNN architectures, namely VGG16, VGG19, and ResNet50. The authors used the BreakHis dataset [18], which consists of 7909 breast cancer histopathology images. The authors opted for binary classification, and the original last classification layer of all the architectures was removed and replaced by a logistic regression classifier. The authors used the AUC of the ROC curve, accuracy, precision, recall, F1 score, and APS as evaluation criteria. The authors tried three different splitting techniques, namely 90% and 10% for training and testing respectively, 80% and 20%, and 70% and 30%. The best result reported by the authors was found using the VGG16 architecture, which achieved an AUC of 95.65% for the first splitting technique, followed by the VGG19 architecture, which achieved an AUC of 91.85% for the first splitting technique as well. The ResNet architecture's performance did not improve by using transfer learning. [PERSON] et al. [19] proposed a novel model to classify histopathology images. The authors used four datasets, namely PatchCamelyon [20; 21], BreakHis [18], Bach [22], and BioImaging [23], to train and validate their model. The authors proposed a novel binary-classification ensemble model composed of three CNN architectures, namely, VGG19 [24], MobileNet [25], and DenseNet [26]. The accuracy results reported by the authors were 98.13%, 95%, 94.64%, and 83.10% for datasets BreakHis [18], Bach [22], PatchCamelyon, and BioImaging, respectively. Different image augmentation techniques were used to increase the size of the training dataset to make the CNN models more robust against overfitting. Some of the augmentation techniques applied were flipping the images horizontally and vertically, increasing the zoom range, and rotating the images. The authors opted for an Adam optimizer with a learning rate of 0.0001, and the batch size used was 32. All of the images were resized to \(224\times 224\)_pixels_, and all of the models were trained for 1000 epochs. The authors claimed that by using the three-model ensemble, the accuracy of the BreakHis dataset increased from 97.42% for the best single classifier to 98.13%, for the PatchCamelyon dataset, it increased from 90.84% to 94.64% for the best single classific; for the Bach dataset, it increased from 92% to 95%, and for the BioImaging dataset, it increased from 81.69% to 83.10%. [PERSON] et al. [27] investigated the effect of transfer learning on the Bach [22] dataset. The authors compared two CNN architectures, namely InceptionV3 [28] and ResNet50 [29], and opted for multi-class classification into four categories. The batch size used was 32, and the optimizer used was stochastic gradient descent with Nesterov momentum with a learning rate of 0.0001, with the dataset trained for 100 epochs for both architectures. The authors reported that the fine-tuned ResNet50 architecture outperformed the InceptionV3 architecture with an accuracy of 97.50% and 91.25%, respectively. [PERSON] et al. [30] used the BreakHis [18] dataset to investigate the effect of transfer learning. The authors opted for the AlexNet and VGG16 architectures. The authors conducted three experiments, two of them using the AlexNet and VGG16 for feature extraction, concatenating their results then adding the SVM classifier, and the third fine-tuning the AlexNet network. The optimizer authors used was SGD with momentum, and the learning rate chosen was 0.0001. The authors set the batch size at 10. The authors reported that the fine-tuned AlexNet outperformed the feature extraction of both the AlexNet and VGG16 networks. [PERSON] et al.