Development of Convolutional Neural Networkbased models for bone metastasis classification in nuclear medicine

Abstract

Focusing on prostate cancer patients, this research paper addresses the problem of bone metastasis diagnosis, investigating the capabilities of convolutional neural networks (CNN) and transfer learning. Considering the wide applicability of CNNs in medical image classification, VGG16 and DenseNet, as being two efficient types of deep neural networks, are exploited for images recognition, being used to properly classify an image by extracting its insightful features. The purpose of this study is to explore the capabilities of transfer learning in VGG16 and DenseNet application process, which will be able to classify bone scintigraphy images in patients suffering from prostate cancer. Efficient VGG16 and DenseNet architectures were built based on a CNN exploration process for bone metastasis diagnosis and then were employed to identify the metastasis from the bone scintigraphy image data. The classification task is a three-class problem, which classifies images as normal, malignant, and healthy images with degenerative changes. The results revealed that both methods are sufficiently accurate to differentiate the metastatic bone from degenerative changes as well as from normal tissue.