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![]() Title:Evaluation of Data Balancing Techniques in 3D CNNs for the Classification of Pulmonary Nodules in CT Images Authors:Thiago Jose Barbosa Lima, Flavio Henrique Duarte de Araujo, Antonio Oseas Carvalho Filho, Ricardo De Andrade Lira Rabelo, Rodrigo De Melo Souza Veras and Mano Joseph Mathew Conference:IEEE ISCC 2020 Tags:3D CNN, Cost function modification, Data augmentation, Imbalanced data and Pulmonary nodules Abstract: Lung cancer is the most prevalent cancer in the world and early detection and diagnosis enable more treatment options and a far greater chance of survival. In this work, we propose an algorithm based on 3D Convolutional Neural Network (CNN) to classify pulmonary nodules as benign or malignant in computed tomography images. Three architecture of 3D CNNs are proposed, containing different input sizes and numbers of convolutional layers. In addition, we investigated data augmentation techniques and modifications in the network training cost function to address the problem of imbalanced data. The best result was achieved for input size of 32X32X32 pixels, 2 blocks of convolutional layers and 2 pooling layers. Also, the modification of cost function achieved promising results, with accuracy of 0.9188, kappa of 0.8019, sensitivity of 0.8481, specificity of 0.9479 and AUC of 0.8980 in the test set during malignant nodule detection. Evaluation of Data Balancing Techniques in 3D CNNs for the Classification of Pulmonary Nodules in CT Images ![]() Evaluation of Data Balancing Techniques in 3D CNNs for the Classification of Pulmonary Nodules in CT Images | ||||
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