Research & Reviews: Journal of Engineering and Technology
MenuISSN: 2319-9873
ISSN: 2319-9873
+44 7456 035580David Javier Benavente Rios
University of Santiago, Chile
ScientificTracks Abstracts: RRJET
Abstract: On the present work we use two different convolutional neural nets architectures for the classification of chest computed tomography images with pleural effusion disease. We decided to use the convolutional neural networks due to the great advances achieved by this kind of nets in image classification problems. We work with a real-world data anonymized and provided by an Imagenology Department of public hospital from Chile. The data was classified by medics of the hospital. Due to the limitations on graphics resource, we decided training the algorithms from scratch, avoiding overfitting with regularization techniques and optimizing the training process with callbacks. For testing, we used a set of 1,000 images and evaluate with classification metrics like True positive rate, True negative rate and Accuracy. Results achieved were not optimal due to overfitting of algorithms. For future works, we will use other architectures of convolutional neural networks and with Transfer learning technique on the architectures. Conclusion: Results achieved were not optimal due to over fitting of algorithms. For future works, we will use other architectures of convolutional neural networks and with Transfer learning technique on the architectures.
David Javier Benavente Ríos is a master’s degree student from University of Santiago Chile. Specialized in machine learning (supervised and unsupervised algorithms) and deep learning for image processing. With experience in different industries. Currently working in machine learning projects for a mining industry. Interested in a PhD position related to computer vision or medical images.