An Enhanced Deep Learning Technique for Prostate Cancer Identification Based on MRI Scans

TL;DR

This paper introduces a two-stage MRI-based deep learning method using InceptionResNetV2 for prostate cancer detection, achieving high accuracy and AUC, and demonstrating improvements over previous techniques.

Contribution

The paper proposes a novel two-stage MRI analysis technique utilizing InceptionResNetV2 for improved prostate cancer diagnosis.

Findings

Accuracy of 89.20% achieved

AUC of 93.6% achieved

Outperforms previous methods

Abstract

Prostate cancer is the most dangerous cancer diagnosed in men worldwide. Prostate diagnosis has been affected by many factors, such as lesion complexity, observer visibility, and variability. Many techniques based on Magnetic Resonance Imaging (MRI) have been used for prostate cancer identification and classification in the last few decades. Developing these techniques is crucial and has a great medical effect because they improve the treatment benefits and the chance of patients' survival. A new technique that depends on MRI has been proposed to improve the diagnosis. This technique consists of two stages. First, the MRI images have been preprocessed to make the medical image more suitable for the detection step. Second, prostate cancer identification has been performed based on a pre-trained deep learning model, InceptionResNetV2, that has many advantages and achieves effective results. In this paper, the InceptionResNetV2 deep learning model used for this purpose has average accuracy equals to 89.20%, and the area under the curve (AUC) equals to 93.6%. The experimental results of this proposed new deep learning technique represent promising and effective results compared to other previous techniques.