COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

Zubair

2022

Imaging techniques such as CT scans have found widespread application in kidney diagnosis. These imaging techniques can estimate kidney size, shape, and position; provide information about kidney function; and assist in diagnosing structural abnormalities such as cysts, stones, and infections. However, different operators have different levels of success when it comes to using CT scans to diagnose renal conditions. The images can be interpreted in various ways due to factors such as the abilities and experiences of the operators, variances in how individuals see the images, and changes in the characteristics utilized for diagnosis. The detection of chronic renal disease might be improved with automated approaches and computer-aided diagnosis systems; however, research into these methods has been limited. According to the findings of this research, the Random Forest classifier has the highest level of accuracy (96.33%) among the various Machine Learning classifiers. As a result, the researchers concluded that chronic renal disease might have been caused by its acquisition. The outcomes of this study indicate that further research should be conducted. Suppose these suggested algorithms

Section: Methodsmentioning

confidence: 99%

“…Computer science is also putting his part in it. Computer science is a vast field, so we select machine learning to detect chronic kidney disease early by using a different methodology [7][8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Classification of Chronic Kidney Disease Using CT Scan Images

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

Zubair

2022

“…In this step, four different types of features are fused. After the fusion of multi-feature, the worth of the dataset was increased, and the previous step of optimization and classification was implemented on the optimized fused dataset [8][9].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Automated Detection of Diabetic Retinopathy Using Retinal fundus images.

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

Anam

et al. 2022

Diabetic retinopathy is the most well-known side effect of diabetes (DR). People with diabetes experience it and can observe how it affects human sight. Patients with DR have damaged blood vessels in the retina, the delicate layer at the back of the eyes. Even though it may not initially show symptoms or cause mild vision problems, DR can cause blindness if not treated. In this study, the classification of the retina based on texture analysis is used to examine the various phases of DR, including mild, moderate, non-proliferative, proliferative, and regular human eye. Stages of DR show misunderstandings about the body. As a result, it might be difficult for a doctor to tell which stage of DR a patient is going through. In order to recognize and classify DR phases, this work suggests an automated approach that combines machine learning (ML) and image processing (IP) techniques. The m has been created for texture analysis utilizing a data fusion approach. A multi-feature dataset and an ML classifier were used to create the model (using cross-validation 10). The multi-layer perceptron (MLP) has shown an extremely high degree of performance with a classification accuracy of 98.53%.

“…The extraction of certain features makes use of a wide variety of strategies and procedures for the preprocessing of images in order to get the best possible results. The proposed approach, together with CVIPtools version 5.6e and WEKA, was used to achieve the results obtained and discussed in this section [9][10]. These are the most effective software applications for feature selection and results generation in image processing, notably in the medical sciences.…”

Section: Methodsmentioning

confidence: 99%

Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

Anam

2022

Nephrolithiasis, commonly known as kidney stone disease, is a disorder in which the deposition of certain minerals causes a stone to develop in the urinary tract. Urolithiasis is another name for nephrolithiasis. Most of the time, kidney calculi form in the renal system and are eliminated through the urine system. Even a tiny stone can readily travel through the urine without any issues. More than 5 millimeters (0.2 inches) in diameter, fully grown calculi can clog the urinary tract, which can cause intense pain in the lumbar region or the stomach.Calculi can result in problems such as dysuria, vomiting, and hematuria. The recommended approach for automatically segmenting kidney stones is based on a four-stage framework, the first of which calls for preprocessing kidney pictures for better enhancement and is followed by the active contour method for automatically segmenting kidney stones. In the future, an assessment will be performed depending on the size and kind of stone.