scholarly journals Electrofused magnesium oxide classification using digital image processing and machine learning techniques

2009 ◽  
Author(s):  
A. B. M. Shawkat Ali ◽  
W. K. Daniel Pun
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Magnesium Oxide ◽  
Digital Image Processing ◽  
Digital Image ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine Learning Techniques for Prediction of Lung Cancer

2020 ◽  
Vol 8 (6) ◽  
pp. 241-249
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Techniques ◽  
Image Dataset

Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.

scholarly journals HEURISTIC ALGORITHM FOR PET IMAGES’ SEGMENTATION USING ARTIFICIAL INTELIGENCE TECHNIQUES

Anales AFA ◽  
2021 ◽  
Vol 31 (4) ◽  
pp. 165-171
Author(s):  
I. E. Scarinci ◽  
◽  
P. Pérez ◽  
M. Valente ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Nuclear Medicine ◽  
Digital Image Processing ◽  
Heuristic Algorithm ◽  
Digital Image ◽  
Data Science ◽  
Machine Learning Techniques ◽  
Patient Specific ◽  
Unsupervised Segmentation

The overall quantity of nuclear medicine procedures has increased remarkably in recent years, making them a daily tool capable of reaching wide sectors of the population. Regarding the nuclear medicine therapeutic applications, it is worth noting that there is an increasing demand of novel techniques and greater variety of radioisotopes requiring accurate patient-specific dosimetry aimed at evaluating lethal damage to the tumor while maintaining acceptable dose levels in healthy tissues. Image-guided internal dosimetry appears as particularly suitable for theranostics procedures, which allow the joint implementation of diagnose and treatment. In this case, the correct segmentation of the images is critical for the identification of different tissues and organs. On the other hand, modern tools based on data science and artificial intelligence have spread in several fields, particularly in the digital image processing. The use of machine learning models for digital image processing appears as a promising opportunity to complement clinical analysis by experts. This paper reports about an unsupervised segmentation heuristic algorithm using clustering and machine learning techniques together, based on the use of two algorithms: K-Means and HDBSCAN. The results obtained highlight the capacity of automatic segmentation by means of clustering algorithms, becoming a useful tool to assist clinician experts and shorten the segmentation times.

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

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