The sooner the patient receives a diagnosis for their condition, the higher their chances will be of surviving it. As is the case with conventional diagnosis, medical imaging is analyzed by trained professionals who look for any signs that the body may be displaying cancerous tendencies. The great quality and multidimensionality of MRI images need the use of an appropriate diagnostic system in addition to CAD tools. Because it is useful, researchers are now concentrating their efforts on developing methods to improve the accuracy, specificity, and speed of these systems. A model that is efficient in terms of image processing, feature extraction, and machine learning is presented in this study. This chapter presents machine learning techniques for prostate cancer detection by analyzing MRI images. Image preprocessing is done using histogram equalization. It improves image quality. Image segmentation is performed using the fuzzy C means algorithm. Features are extracted using the gray level co-occurrence matrix algorithm. Classification is performed using the KNN.
TopIntroduction
The prostate is a somewhat unremarkable organ in the human reproductive system, yet it plays an essential role. Sperm are carried throughout the male reproductive system by the fluid that is generated by the prostate gland and known as semen. It is situated between the urinary bladder and the upper urethra, which is the conduit via which urine is passed from the urinary bladder. Prostate cancer (PC) is the most common non-melanoma cancer in men, and it has emerged as one of the most pressing issues facing public health on a worldwide scale. An uncontrolled growth of cells inside the prostate gland is what leads to the development of prostate cancer (Vilanova et. al., 2017).
Cancers of the peritoneal cavity may progress in one of two ways: gradually or swiftly. Tumors with a slow growth rate often exclusively affect the prostate. About 85 percent of all occurrences of pancreatic cancer are caused by forms of tumours that grow slowly. In the treatment of these circumstances, active monitoring is an absolutely necessary component (Cameron et. al., 2016). The second kind of pancreatic cancer, in contrast to the first, grows swiftly and metastasizes to other areas of the body via a process called spread. Monitoring techniques that can be relied on are required in order to accomplish the task of differentiating between these two types of evolution. In most cases, the early detection of PCs is accomplished by the performance of routine physical tests. The first thing that has to be done in order to devise a treatment plan is to pinpoint the precise location of the prostate. In order to achieve a high survival rate, screening approaches that are both effective and dependable are used. The PSA test, transrectal ultrasonography, and magnetic resonance imaging (MRI) are the three types of prostate cancer screening that are being used the most often (Jasti et. al., 2022).
While the first guideline was solely concerned with classifying clinical relevance, the revisions to the original prostate MR guidelines centered on developing global standards for MRI. This contrasts with the primary emphasis of the original guideline. The degree of photo capturing and reporting is intended to be brought up to date with each new release, which is the aim. Recent research has conducted a number of studies that investigated the impact of suggestions that were developed based on these criteria. Any one of the following methods may be used to classify a clinically significant PC lesion: However, there are specific constraints to consider when classifying lesions that are quite tiny but rather severe. It has been established that a PI-RADS guideline may assist to detect the cancer that spreads outside of the prostate, which has a significant influence on the staging of cancer. This is because the disease has spread outside the prostate (Giannini et. al., 2017).
The biological databases include a tremendous amount of information for researchers to peruse (Chaudhury et. al., 2022). It is getting more challenging to gain insights from the massive amounts of data that are being collected. Machine learning is a kind of learning in which a machine utilizes examples, comparisons, and past experience to improve itself. This type of learning came about as a result of the fact that data mining has become such an important component of knowledge mining. The fundamental concept behind machine learning is pattern recognition in data and the ability to draw quick conclusions based on a variety of different datasets. Using methods derived from machine learning, automated screening of ligand libraries may be carried out (Weinreb et. al., 2016, Zamani et. al., 2022).
This article presents machine learning techniques for prostate cancer detection by analyzing MRI images. Image preprocessing is done using histogram equalization. It improves image quality. Image segmentation is performed using the fuzzy C means algorithm. Features are extracted using the Gray Level Co-occurrence Matrix algorithm. Classification is performed using the KNN, Random Forest and Adaboost algorithms.