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

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.

Learning for early childhood using the IcanDO platform: Breakthroughs for golden age education in Arabic learning.

Early childhood is an age of children whose learning tendencies rely on games, therefore this study aims to describe the IcanDO platform for early childhood in learning Arabic. The research was conducted with a qualitative approach, the data sources were early childhood teachers. Data collected by observation techniques, interviews and documentation studies. The result of the research is that IcanDO as a learning platform is interesting for early childhood, with this platform they can play to learn Arabic. In accordance with the data, it was found that the IcanDO platform used can stimulate early childhood learning, stronger memory, personalized learning can be implemented, children's thinking skills can be trained and children's multilingual abilities are also growing. Researchers recommend that the use of various platforms that support the implementation of education in difficult conditions continue to be developed. The limitation of this research is that IcanDO and its effectiveness have not been studied comprehensively, therefore the aspects that have not been touched in this research can be the work of future researchers.

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection.

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.