Analysis of filtering and novel technique for noise removal in MRI and CT images

Pillai, Anitha S.; Kola, Laxminarayana; Sushma, P.; Archana, S.

doi:10.1109/iceeccot.2017.8284618

Cited by 17 publications

(8 citation statements)

References 1 publication

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“…Gaussian lters [15], histogram equalization[16], and the median lter are among popular ltering methods in image processing for removing noises common in MRI images [17,18].…”

Section: Data Preprocessing and Augmentationmentioning

confidence: 99%

Classification of prostate cancer using Deep Learning approach and MobileNetV2 architecture

Mashak

Akbarizadeh

Farshidi

2022

Preprint

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Since prostate cancer is one of the most important causes of mortality in today's society, the study of why and how to diagnose and predict them has received much attention from researchers. The collaboration of computer and medical experts offers a new solution in analyzing this data and obtaining useful and practical models, which is data mining. In fact, data mining, as one of the most important tools for data analysis and discovering the relationships between them and predicting the occurrence of events is one of the practical tools of researchers in this way. This study diagnoses and classifies prostate cancer using Deep Learning approach and MobileNetV2 architecture based on a method to identify the factors affecting this disease. In this study, data was taken from a database on the Brigham Hospital website. Also, in order to improve the methods of diagnosing prostate cancer, a feature-classification approach has been proposed, which has been evaluated using a data set related to clients' files. The proposed method after applying various classification methods on the available data including benign and malignant diagnosis and reaching an optimal method with relatively high accuracy using a faster R-CNN network to segment the area and later using architecture Various convolutional neural networks (CNNs) have been selected for feature extraction and set classification, increased processing speed. In addition, the MobileNetV2 architecture is used, which has the ability to achieve AUC in the range of 0.87 to 0.95 with acceptable performance, high processing speed and relative accuracy for the diagnosis of prostate cancer.

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“…Gaussian lters [15], histogram equalization[16], and the median lter are among popular ltering methods in image processing for removing noises common in MRI images [17,18].…”

Section: Data Preprocessing and Augmentationmentioning

confidence: 99%

Classification of prostate cancer using Deep Learning approach and MobileNetV2 architecture

Mashak

Akbarizadeh

Farshidi

2022

Preprint

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“…The raw data collected from the sensors are normally noisy which will significantly degrade the performance of activity recognition. Filtering techniques are often used to mitigate the influence of these unwanted noise [47]. The most common filtering methods include mean filter, low-pass filter, Gaussian filter, wavelet filter and Kalman filter, etc.…”

Section: ) De-noisingmentioning

confidence: 99%

A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost

Zhang

Zhao

2019

IEEE Access

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Recently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor changes and the improvement of positioning accuracy according to indoor landmarks. However, we have not found a comprehensive study for recognizing indoor activities related to multi-floor indoor positioning based on a robust machine learning algorithm. In this work, we propose a framework for recognizing five indoor activities, i.e., walking, stillness, stair climbing, escalator, or elevator taking. In this framework, we investigate the relevant sensors and features to improve the recognition accuracy of these activities, especially some specific features in the frequency domain and wavelet domain. We propose to utilize a promising tree-based ensemble learning classifier, XGBoost, to recognize these activities. Based on our dataset created by 40 volunteers, we provide a comprehensive analysis of the proposed framework for indoor activity recognition. Considering both accuracy and computational cost, the XGBoost-based indoor activity recognition algorithm outperforms the other ensemble learning classifiers and single classifiers, and the average recognition F-score of XGBoost reaches 84.41%. In addition, our introduced specific features in the frequency domain and wavelet domain can significantly improve the recognition accuracy. Moreover, we use a publicly available dataset to verify our proposed framework and XGBoost classifier reaches 84.19% that outperforms the other classifiers.

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“…Detection of lung cancer can be done in several ways, such as using computed tomography (CT) and MRI [7]. For human bodies, organs such as the lungs, liver, pancreas, kidney, and bone, it is most appropriate to use CT scans because CT scans produce cross-section images of the body using computed technology and x-rays that play a key role in the diagnosis of medical conditions [7]. Besides, CT scan is more preferred because it is easy to use and provides accurate classification and foreign mass location [7].…”

Section: Introductionmentioning

confidence: 99%

“…For human bodies, organs such as the lungs, liver, pancreas, kidney, and bone, it is most appropriate to use CT scans because CT scans produce cross-section images of the body using computed technology and x-rays that play a key role in the diagnosis of medical conditions [7]. Besides, CT scan is more preferred because it is easy to use and provides accurate classification and foreign mass location [7]. The use of MRI is expensive, less available as compared to CT scans, and time-consuming diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours

et al. 2022

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Lung cancer is the prevalent cause of death among people around the world. The detection of the existence of lung cancer can be performed in a variety of ways, such as magnetic resonance imaging (MRI), radiography, and computed tomography (CT). Such techniques take up a lot of time and financial resources. Nevertheless, for the detection of lung cancer, CT provides a lower cost, fast imaging time, and increased availability. Early diagnosis of lung cancer may help physicians treat patients to minimize the number of deaths. This paper revolves around the categorization of lung cancer stages from CT scan images using image processing and k-Nearest Neighbor. The central objective of this study is therefore to establish an image processing technique for extracting features of lung cancer from CT scan images. Extracting the features from the segmented image can help to detect cancer inside the lung. The purposed method comprises the following steps by using image processing techniques: data collection, data pre-processing, features selection, and lung cancer classification. The pre-processing was done using a median filter to remove noise contained in the images. Three features need to be extracted which are area, perimeter, and centroid. Finally, the set of data with these features were used as inputs for lung cancer classification. By analysis results, the kNN method has a high accuracy of 98.15%.

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Analysis of filtering and novel technique for noise removal in MRI and CT images

Cited by 17 publications

References 1 publication

Classification of prostate cancer using Deep Learning approach and MobileNetV2 architecture

Classification of prostate cancer using Deep Learning approach and MobileNetV2 architecture

A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost

Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours

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