[Retracted] Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images

Abstract

Cancer has a disproportionately large influence on the death rate of adults. A patient needs to get a diagnosis of their condition as quickly as is humanly feasible in order to have the greatest chance of surviving their sickness. Skilled medical professionals use medical imaging and other traditional diagnostic methods to search for clues that may indicate the presence of malignant tendencies inside the body. Nevertheless, manual diagnosis may be time-consuming and subjective owing to the wide range of interobserver variability induced by the enormous number of medical imaging data. This variability is caused by the fact that medical imaging data are collected. Because of this, the process of accurately diagnosing a patient could become more difficult. To execute jobs that included machine learning and the interpretation of complicated imagery, cutting-edge computer technology was necessary. Since the 1980s, researchers have been working on developing a computer-aided diagnostic system that would help medical professionals in the early diagnosis of various malignancies. According to the most recent projections, prostate cancer will be discovered in the body of one out of every seven men at some time throughout the course of their life. It is unacceptable how many men are being told that they have prostate cancer, and the condition is responsible for the deaths of a rising number of men every year. Because of the high quality and multidimensionality of the MRI pictures, you will also need a powerful diagnosis system in addition to the CAD tools. Since it has been shown that CAD technology is beneficial, researchers are looking at methods to improve the accuracy, precision, and speed of the systems that use it. The effectiveness of CAD technology has been shown. This research proposes a strategy that is both effective and efficient for the processing of images and the extraction of features as well as for machine learning. This work makes use of MRI scans and machine learning in an effort to detect prostate cancer at an early stage. Histogram equalization is used while doing the preliminary processing on photographs. The image’s overall quality is elevated as a result. The fuzzy C means approach is used in order to segment the images. Using a Gray Level Cooccurrence Matrix (GLCM), it is feasible to extract features from a dataset. The KNN, random forest, and AdaBoost classification algorithms are used in the classification process.

Evaluation of Prostate Cancer via Machine Learning

“…The process of assigning and renewing centers continues until no changes occur. Thus, the number of classes forming a cluster is denoted by the letter k. The most important thing to keep in mind when using the kNN method is that it always produces the same results, even when using the same learning data (Anand, 2023).…”

“…Currently, there is no early diagnosis method that can reveal that a healthy man will have PCa. With the use of some advanced technological methods (such as Multiparametric magnetic resonance (MR), MR-Ultrasound Fusion Biopsy), it is often diagnosed with a PSA (Prostate Specific Antigen) test, which is examined with a blood sample, or by prostate biopsy (Mydlo et al, 2016;Anand et al, 2023). However, the PSA test can cause pointless, expensive and painful needle biopsies.…”

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

“…In these models, the desired output may be a class label, such as the status of the cell or tissue (i.e., damaged or intact), or a continuous variable, such as the value of a biochemical or immunological parameter. Some successfully developed ML models encompassing GLRLM or GLCM data include random forests and gradient boosting regressor algorithms, as well as multilayer perceptron artificial neural networks [12][13][14][15] .…”

Retracted: Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Manipulated or compromised peer review …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”