Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification

Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. For PCa diagnosis, Multi-parametric magnetic resonance imaging (mpMRI) emerged as an invaluable imaging modality that offers a precise anatomical view of the prostate gland and its tissue structure. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. Recently, DL techniques have been employed to develop a pipeline for segmenting and classifying different cancer types. These studies show that DL can be used to increase diagnostic precision and give objective results without variability. This work uses well-known DL models for the classification and segmentation of mpMRI images to detect PCa. Our implementation involves four pipelines; Semantic DeepSegNet with ResNet50, DeepSegNet with recurrent neural network (RNN), U-Net with RNN, and U-Net with a long short-term memory (LSTM). Each segmentation model is paired with a different classifier to evaluate the performance using different metrics. The results of our experiments show that the pipeline that uses the combination of U-Net and the LSTM model outperforms all other combinations, excelling in both segmentation and classification tasks.Comment: Accepted at CISCON-202

MULTISPECTRAL SUPER RESOLUTION AND IMAGE QUALITY ASSESSMENT COMPARATIVE ANALYSIS

The satellite image resolution alludes to highest accuracy to capture finer details from scene. This paper addresses five different techniques to improve resolution of multispectral satellite image. Our algorithm generates super resolved multispectral image using advantages of Patch Based processing. The results are then compared with four techniques Bicubic Interpolation, Edge Directed Orientation, Patch Based Processing, Gaussian Process Regression (GPR). Comparative analysis is carried out with reference to Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity, correlation coefficient and similarity measure, processing speed and storage space required. Image quality assessments (IQA) parameters are also performed. Super resolution (SR) is commercial algorithm to improve resolution of satellite image when we compare with image fusion

Multispectral Super Resolution and Image Quality Assessment Comparative Analysis

The satellite image resolution alludes to highest accuracy to capture finer details from scene. This paper addresses five different techniques to improve resolution of multispectral satellite image. Our algorithm generates super resolved multispectral image using advantages of Patch Based processing. The results are then compared with four techniques Bicubic Interpolation, Edge Directed Orientation, Patch Based Processing, Gaussian Process Regression (GPR). Comparative analysis is carried out with reference to Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity, correlation coefficient and similarity measure, processing speed and storage space required. Image quality assessments (IQA) parameters are also performed. Super resolution (SR) is commercial algorithm to improve resolution of satellite image when we compare with image fusion

Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support

Prostate cancer (PCa) is a significant health concern for men worldwide, where early detection and effective diagnosis can be crucial for successful treatment. Multiparametric magnetic resonance imaging (mpMRI) has evolved into a significant imaging modality in this regard, which provides detailed images of the anatomy and tissue characteristics of the prostate gland. However, interpreting mpMRI images can be challenging for humans due to the wide range of appearances and features of PCa, which can be subtle and difficult to distinguish from normal prostate tissue. Deep learning (DL) approaches can be beneficial in this regard by automatically differentiating relevant features and providing an automated diagnosis of PCa. DL models can assist the existing clinical decision support system by saving a physician’s time in localizing regions of interest (ROIs) and help in providing better patient care. In this paper, contemporary DL models are used to create a pipeline for the segmentation and classification of mpMRI images. Our DL approach follows two steps: a U-Net architecture for segmenting ROI in the first stage and a long short-term memory (LSTM) network for classifying the ROI as either cancerous or non-cancerous. We trained our DL models on the I2CVB (Initiative for Collaborative Computer Vision Benchmarking) dataset and conducted a thorough comparison with our experimental setup. Our proposed DL approach, with simpler architectures and training strategy using a single dataset, outperforms existing techniques in the literature. Results demonstrate that the proposed approach can detect PCa disease with high precision and also has a high potential to improve clinical assessment