Dec 20, 2019 · In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient.

In the present study, a Deep learning convolution neural network (CNN) model was employed and performance was compared with machine learning techniques to detect the Prostate Cancer using Matlab version 2017b. A transfer learning approach was used to train the cancer images using GoogleNet from CNN.

The literature presented a deep learning network composed of an adversarial neural network and a U net for the automated segmentation of prostate cancer lesions using MP-MRI. The proposed method plays a crucial role for early detection of prostate cancer sites.

D. Nguyen, et al. proposed a CNN model using U-net by modifying the architecture to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients along with the average mean and max dose differences of all …

As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer....

To address these issues, this paper proposes the Data, Classification Technique, Prediction, and View (DCPV) taxonomy, which outlines the primary components required to implement and validate a...

Accurate detection of prostate cancer is a major medical concern globally. Koonamparampath et al. [1] conducted a review on prostate cancer screening with CNN, where prostate cancer is the second leading cause of death in men. It is usually asymptomatic until advanced stages.

Abstract: A computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) is proposed in this paper. The proposed system starts by defining a region of interest that includes the prostate across the different slices of the input DWI volume.

In this study, a modified CNN system is proposed to develop an automated algorithm capable of detecting clinically significant prostate cancer using DWI images of patients. The study employed a clinical database consisting of 970 DWI images from individuals, with 17 cases diagnosed with prostate cancer (PCa) and 14 cases considered healthy.

Block diagram of the proposed pipeline for prostate cancer detection. The inputs to each CNN are 66 × 66 × 6 (ADC, b0, b100, b400, b1000, b1600) MRI slices. The output is the slice level and patient level results.