Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Abstract

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

Special Issue “Computer Aided Diagnosis Sensors”

“…Alyoubi et al [ 28 ] proposed a screening system for DR fundus image classification and lesions Localization to help ophthalmologists determine the patients’ DR stage. Abdelmaksoud et al [ 29 ] developed a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI). The identification of prostate cancer was achieved using two previously trained CNN models (AlexNet and VGGNet) that were fed with the estimated ADC maps of the segmented prostate regions.…”

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

“…CNN and TL have been widely used in the prediction of medical conditions using different techniques (CT, MRI, panoramic images)-for example: identification of prostate cancer [19]; prediction of bladder cancer treatment response in CT [20]; detection of maxillary sinusitis on panoramic radiographs [21]; screening for osteoporosis in dental panoramic radiographs (DPR) [22]; cardiac cine segmentation [23] and even COVID-19 detection from chest CTscans [24]. Furthermore, Kats et al have recently shown the potential of applying CNN to detect CAC, using Faster Region-based Convolutional Neural Network (FR-CNN) [25] on a modest set of 65 DPRs reaching a F1 score of 0.77 [26].…”

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

“…CNN and TL have been widely used in the prediction of medical conditions using different techniques (CT, MRI, panoramic images) -for example: identification of prostate cancer [12]; prediction of bladder cancer treatment response in CT [13]; detection of maxillary sinusitis on panoramic radiographs [14]; screening for osteoporosis in dental panoramic radiographs [15]; cardiac cine segmentation [16] and even COVID-19 detection from chest CT-scans [17].…”