# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 1 Introduction  
Medical images play a very crucial role in patient treatment; however, usually, the shortage of manpower, the time required to reach a decision, and the need for a second opinion are factors that greatly impact the process. Correctly and more quickly classifying images is an absolute need for certain medical images fields, like pathology. Histopathology images are very important for detecting certain kinds of diseases like cancer or even determining the kind of cancer itself to see if it is benign or malignant and its degree. Histopathology is defined as examining a tissue sample taken by a biopsy to diagnose certain diseases microscopically [1]. It plays a very important role in the detection of diseases, enabling doctors to carefully create a treatment plan. The physician who is responsible for classifying histopathology images is called a pathologist. The image-examining process is extremely difficult and requires an experienced pathologist with years of experience. In the United States, the number of active pathologists dropped 17.5% in the last decade while the workload increased by 41% [2], which indicates a real need for assisting the pathologists in their work by providing them with an autonomous classifier that is able to classify histopathology images with a high level of accuracy. A recent breakthrough in the artificial intelligence field is machine learning, in which an algorithm can be developed that will be able to extract image features automatically. When the algorithm used isa neural network with more than one hidden layer, it is called deep learning. Deep learning can be implemented in the image classification domain in which a feed-forward convolutional neural network (CNN) [3] can be used to classify images automatically. Making the CNN able to classify images is called training; in training, the CNN's weights will be adjusted to suit the image dataset under study. The main point of CNN is that it is able to map important features of images that can be used to classify those images without the CNN's being explicitly programmed to do so. CNN has been proven to work with great accuracy in the classification of many medical domains like diabetic retinopathy detection and classification [4; 5], Alzheimer's disease detection [6; 7], and skin lesion detection [8; 9], among others. Image classification, which is defined as grouping images into successive predefined labels, plays a very important role in many areas, like the medical field. Deep learning algorithms can detect important features of images without any manual feature engineering, which can be thought of as using autonomous algorithms that can learn by themselves how to differentiate between distinct image classes. The earliest attempt to construct an automatic classifier that could learn how to differentiate between classes was introduced by [PERSON] [3], who was inspired by the work of [PERSON] et al. [10] and [PERSON] and [PERSON] [11], and was named convolutional neural networks (CNN), but this attempt was limited because of the size of datasets and the computational power available then. In 2012, [PERSON] et al. [12] introduced their CNN architecture that was named AlexNet and won first place in the ILSVRC competition [13], with an error rate of 16% compared to the second place winner's 25%, and since then, CNN has become a state-of-the-art image classifier. CNN is considered a feed-forward artificial neural network that has at least one convolution layer. A diagram of CNN is shown in Figure 1.  
To accurately train CNN and to overcome the overfitting issues associated with feed-forward neural networks, usually, hundreds of thousands or even millions of images are needed [14], which can limit the usage of CNN to certain domains, like nature images, for classification.