# Evolving Optimised Convolutional Neural Networks for Lung Cancer Classification
###### Abstract  
Detecting pulmonary nodules early significantly contributes to the treatment success of lung cancer. Several deep learning models for medical image analysis have been developed to help classify pulmonary nodules. The design of convolutional neural network (CNN) architectures, however, is still heavily reliant on human domain knowledge. Manually designing CNN design solutions has been shown to limit the data's utility by creating a co-dependency on the creator's cognitive bias, which urges the development of smart CNN architecture design solutions. In this paper, an evolutionary algorithm is used to optimise the classification of pulmonary nodules with CNNs. The implementation of a genetic algorithm (GA) for CNN architectures design and hyperparameter optimisation is proposed, which approximates optimal solutions by implementing a range of bio-inspired mechanisms of natural selection and Darwinism. For comparison purposes, two manually designed deep learning models, FractalNet and Deep Local-Global Network, were trained. The results show an outstanding classification accuracy of the fittest GA-CNN (91.3%), which outperformed both manually designed models. The findings indicate that GAs pose advantageous solutions for diagnostic challenges, the development of which may to be fully automated in the future using GAs to design and optimise CNN architectures for various clinical applications. genetic algorithm; evolutionary algorithm; lung cancer; pulmonary nodules; deep learning; medical imaging; computer-aided diagnostics; hyperparameter optimisation +
Footnote †: journal: _Acticle_  
usually multiple hundreds per patient, can make it challenging to identify small, malignant, or suspicious lung nodules in the available timeframe [2, 10]. Pulmonary nodules appear in various shapes with heterogeneous properties, such as diversified densities or malignancy-implicent calcification properties [2]. Hence, further complicating the scanning and classifying of such for radiologists [2]. In this paper, however, the focus lies on improving lung nodule classification performance only, as combinations with additional segmentation CNNs such as U-Net could pose a sustainable approach for a stand-alone CAD system in radiology diagnostics [12, 13, 14]. For the classification of any accumulated cell mass's malignancy that is visible on CT scans, however, specialised machine learning (ML) applications could be used in the future [15, 16]. In particular, deep learning (DL) methods and convolutional neural networks (CNNs) have demonstrated outstanding potential to analyse both feature-complex and large datasets [17], such as AlexNet [18], DenseNet [19], GoogLeNet [20]. Examples of models with outstanding performance on CT-lung scans include Resnet and FractalNet [16, 17]. However, other approaches, such as deep local-global networks (DLGNs), too, have demonstrated remarkable lung nodule classification performance [18, 19]. FractalNet, a CNN approach that does not incorporate residuals, allows repeated use of simple expansion rules, hence supporting the widened depth of image analysis by its truncated fractals [21, 22, 23]. In contrast to the FractalNet, the DLGN relies on shortcuts to help minimise residuals between layers while preserving the identity and weights of the previous layer's outputs upon residual summation [24, 25]. DLGNs have demonstrated to be able to outperform ResNet and other models, partly due to their ability to extract multi-scale features with high generalisation ability [24]. In contrast to other CNN approaches, DLGNs aim to perceive both local and global features without demanding a full connection to all its layers, which helps reduce computing time by cutting down the number of computations of weights and connections. Since many CNNs with small kernel sizes fail to capture global features, DLGNs were developed with the concept of not dilated, but residual convolution [24], which has been proven to help identify both local and global features.