# Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization
## 1 Introduction  
Alzheimer's disease (AD) is the most common cause of dementia, and is one of the major problems of global population ageing. With advances in relevant technologies, there is ongoing research into the diagnosis and treatment of dementia.  
Amyloid, as well as the tau (\(\tau\)) protein, is considered as one of the main causes of AD because this protein is found in brains of the majority of patients with AD. Moreover, as deposition of amyloid occurs gradually and long before the disease is clinically significant, it is regarded as a very important factor of screening for AD [1].  
Standard Uptake Value Ratio (SUVR) [2], is a classical, quantitative analysis method, to analyse Positron Emission Tomography-Computed Tomography (PET/CT). This method calculates the ratio of two mean activity concentrations of Region of Interest (RoI) or Volume of Interest (Vol), and the reference region. These activity concentrations are calculated using the intensity of the image, which has been regularised using patients' body weight and injected radioactivity. SUVR provides an objective and quantitative update ratio. However, it is very difficult to decide RoI/Vol and takes a long time to analyse. Furthermore, generalising the threshold for the diagnosis is impossible due to the noise and inconsistency of devices.  
Recently, with the development of image processing techniques, various deep learning-based technologies are applied across different areas of medical industry [3, 4]. Specifically,image classification techniques extract relevant features from input images via training, and use these features to perform classification.  
If an appropriate algorithm is used for the given domain, a strong performance is observed. However, depending on the statistical distribution of the input data, the error space formed by the loss function is completely different. Therefore, various optimisation techniques are required. [PERSON] et al. [5] suggested a machine learning-based method to search for the best network structure for an input data. It decides the ideal structure for each layer using the information from previous hidden layers.  
Genetic Algorithm (GA) [6] is a computational model that simulates methodologies described from [PERSON]'s theory of evolution, such as natural selection and genetic mechanisms. As GA searches the optimal solution through simulation of the natural evolution process, it is beneficial to use this algorithm for problems that require complicated algorithms to find their solutions. Genetic CNN [7] is a popular example that is developed from GA, which optimises the structure of CNN models. Network structure configured from the algorithm showed similar performances when compared with state-of-the-art classification networks.  
However, one limitation of Genetic CNN is that it does not consider a set of hyperparameters, which affects the rate of convergence of the network on the error surface. Examples of such hyperparameters include activation functions and optimisation algorithms. Appropriate selection of these parameters will lead to faster convergence to the global minimum point on the error surface. This leads to excellent task performance, and also avoids problems such as overfitting.  
In this paper, we show that our version of genetic algorithm is capable of finding the best-performing CNN architecture, including activation function and optimisation algorithm, which is specific for the given input dataset. The input dataset used in the algorithm is the set of PET/CT images with amyloid. As these images are used for early diagnosis of AD, the dataset is appropriate for extracting features of lesions associated with AD.