A Review of Image Segmentation Using MATLAB Environment

Abdulrahman, Awf; Varol, Serkan

doi:10.1109/isdfs49300.2020.9116191

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…Similarly, the process of improvement, recovery, compression or decompression will be induced later according to the needs of the system. [24]. These various techniques are used through the use of filters, histograms, among others, the most important method being filters, which is applied in most images to help reduce the different types of noise that affect them.…”

Section: A Pre-processingmentioning

confidence: 99%

Image Processing System for the Detection of Recyclable Solid Waste

Meneses-Claudio¹,

Muñoz²,

Huamaní³

et al. 2023

IJETAE

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One of the problems that affects everyone is solid waste pollution, which has spread to various parts of the surface and has put the planet in trouble because of the enormous amount of waste that has accumulated over time, affecting the habitats of various marine and terrestrial animals. These damages on the environment, reflect the lack of culture human about recycling, just 10% worldwide recyclemore frequently in homes. Nowadays, the change of materials to biodegradable has been made so that their decomposition is early, accumulations of recyclable materials are still observed in various parts of the surface and observing that it does not solve the contamination. With the exposure of this problem, this article developed an image processing system for the detection of recyclable solid waste that through an automatic analysis will allow the identification of recyclable solid waste materials, such as plastic, cardboard and glass bottles, helping to detect those solid waste that canbe reuse and avoid accumulation of them per ton. Through its development, it was observed that the system identifies the various recyclable materials that are distributed throughout the street with an efficiency of 97.99%, standing out for its efficiency and precision in the analysis of recyclable materials.

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Section: A Pre-processingmentioning

confidence: 99%

Image Processing System for the Detection of Recyclable Solid Waste

Meneses-Claudio¹,

Muñoz²,

Huamaní³

et al. 2023

IJETAE

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“…This size was chosen because the size of all images was greater than or equal to this number. Then the chest area was cropped by selecting the middle point of the images (90 pixels around the center) [15]. The images were thus resized again to 100 × 100.…”

Section: Image Processingmentioning

confidence: 99%

A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

Abdulrahman

Baykara

2022

Applied Sciences

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The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has been used to detect the virus. However, since it is an expensive and slow method, artificial intelligence researchers have attempted to develop quick, inexpensive alternative methods of diagnosis to help doctors identify positive cases. Therefore, researchers are starting to incorporate chest X-ray scans (CXRs), an easy and inexpensive examination method. This study used an approach that uses image cropping methods and a deep learning technique (updated VGG16 model) to classify three public datasets. This study had four main steps. First, the data were split into training and testing sets (70% and 30%, respectively). Second, in the image processing step, each image was cropped to show only the chest area. The images were then resized to 150 × 150. The third step was to build an updated VGG16 convolutional neural network (VGG16-CNN) model using multiple classifications (three classes: COVID-19, normal, and pneumonia) and binary classification (COVID-19 and normal). The fourth step was to evaluate the model’s performance using accuracy, sensitivity, and specificity. This study obtained 97.50% accuracy for multiple classifications and 99.76% for binary classification. The study also got the best COVID-19 classification accuracy (99%) for both models. It can be considered that the scientific contribution of this research is summarized as: the VGG16 model was reduced from approximately 138 million parameters to around 40 million parameters. Further, it was tested on three different datasets and proved highly efficient in performance.

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“…This information appears on a black background as white lines. Several different filtering images are used, such as the Sobel filter, Roberts filter, Prewitt filter, Zero-cross filter, and Canny filter [13]. These techniques are quick to compute, and no pre-existing image quality knowledge is required.…”

Section: Edge-based Techniquesmentioning

confidence: 99%

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Kareem

Al-Sulaifanie

Hasan

et al. 2021

AJRCoS

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A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.

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A Review of Image Segmentation Using MATLAB Environment

Cited by 20 publications

References 10 publications

Image Processing System for the Detection of Recyclable Solid Waste

Image Processing System for the Detection of Recyclable Solid Waste

A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

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