Machine learning techniques for metastatic tumor prediction
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2023
Författare
Dahlén , Filip
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurate prediction of tumor metastasis is crucial for cancer prognosis and treatment. This study explores the application of artificial intelligence techniques, specif ically neural networks and Principal Component Analysis (PCA), for predicting
tumor metastasis based on histopathological images. The aim is to identify an effective approach that balances accuracy and computational efficiency. The study
begins with a comparative analysis between PCA and a Convolutional Neural Network (CNN) for image classification, using the ISIC dataset and MNIST dataset.
To further investigate potential benefits, a combined model using PCA and CNN
is developed. PCA is employed as a dimensionality reduction technique to enhance
computational efficiency. The combined model demonstrates promising results, reducing computational time while maintaining accuracy.
In the second part of the study, metastatic prediction is addressed using Whole
Slide Images (WSI) obtained from Sahlgrenska University Hospital. Preprocessing
involves dividing WSIs into smaller tiles and extracting relevant features such as
breslow depth and metastatic status. Two models are utilized: a modified ResNet18
and a shallower network (ConvNet2/ConvNet3) with varying inputs. Training and
evaluation are performed using a 4-fold cross-validation approach and mini-batches.
It was observed that the PCA algorithm performed similar to the neural networks
but was outperformed in terms of computational time. The combined model showcased the potential in reducing computational time while maintaining accuracy.
The combined model showcased the potential in reducing computational time while
maintaining accuracy when the there are a large amount of available data with high
complexity. Lastly, the study showed that when the data are limited, the best approach was to utilize a neural network based on image data and clinical data to
enhance metastatic prediction.
Overall, this thesis emphasizes the potential of neural networks based on image and
clinical data for enhancing metastatic prediction, providing valuable insights for
improved cancer prognosis and treatment decision-making.