A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients

• Published in: Healthcare (Basel) Vol. 8; no. 4; p. 493
• Main Authors: Ntakolia, Charis, Diamantis, Dimitrios E., Papandrianos, Nikolaos, Moustakidis, Serafeim, Papageorgiou, Elpiniki I.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.11.2020; MDPI
• Subjects: Automation; Bones; Classification; Datasets; Decision making; Deep learning; Metastasis; Neural networks; Nuclear medicine; Patients; Prostate cancer; Scintigraphy; Tomography; Germany; lightweight look-behind fully convolutional neural network; deep learning; nuclear medicine; convolutional neural network; medical image; machine learning; bone metastasis classification
• DOI: 10.3390/healthcare8040493
• ISSN: 2227-9032

Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment.