What are some potential challenges and approaches to improving the performance of a 3D convolutional neural network for lung cancer detection in the Kaggle competition?
One of the potential challenges in improving the performance of a 3D convolutional neural network (CNN) for lung cancer detection in the Kaggle competition is the availability and quality of the training data. In order to train an accurate and robust CNN, a large and diverse dataset of lung cancer images is required. However, obtaining
How is the progress of the preprocessing tracked?
In the field of deep learning, particularly in the context of the Kaggle lung cancer detection competition, preprocessing plays a important role in preparing the data for training a 3D convolutional neural network (CNN). Tracking the progress of preprocessing is essential to ensure that the data is properly transformed and ready for subsequent stages of
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, 3D convolutional neural network with Kaggle lung cancer detection competiton, Preprocessing data, Examination review
How did the speaker calculate the approximate chunk size for chunking the slices?
To calculate the approximate chunk size for chunking the slices in the context of the Kaggle lung cancer detection competition, the speaker utilized a systematic approach that involved considering the dimensions of the input data and the desired output size. This process was essential to ensure efficient processing and accurate results in the 3D convolutional