Implementation of Metastatic Cancer Detection using Supervised Machine Learning
Keywords:
Computer-assisted diagnosis, Convolutional Neural Network (CNN), Deep learning, Digital image analysis, Histopathology, Machine learning, Metastatic cancer, Whole slide imagesAbstract
Cancer is one of the leading causes of death in the world, with an estimated 9.6 million deaths in 2018. Much of this mortality is due to metastasis, the spread of malignant cells from the primary tumour to other parts of the body, so the early detection of metastatic tissue is essential. Pathologists currently find metastases by examining stained tissue sections under a microscope, which is slow and can vary from one observer to another. This article presents a supervised machine learning approach for detecting metastatic tissue in histopathological images of lymph node sections. This study first reviews earlier techniques, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy Neural Networks (FNN), which reported accuracies between 71% and 96%. The study then implements a Convolutional Neural Network (CNN) based on the VGG16 architecture, pre-trained on ImageNet and adapted to 96 × 96 pixel patches from the Kaggle Histopathologic Cancer Detection dataset. The CNN learns its features directly from the image pixels and separates cancerous from non-cancerous patches with an accuracy of 97.35%, which is higher than that of the reviewed methods. The results suggest that CNN-based models can support pathologists in the early detection of metastatic cancer and reduce the time needed for diagnosis.
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