An Efficient and Robust Fracture Detection in Femur Bones

There are several bones in the body but the femur is especially the important bone in the body which is from the hip to knee. The Red blood cells(RBC) are created because of bone called femur. In this paper we have given a method to know where the bone has broken by the methods of image processing..We will preprocess the image in order to show the interested domain . In this paper, foreground is taken as our interested domain in order to hide the background details. There are many mathematical and morphological operations which are used for this process, by using these methods and operations we highlight the foreground and the objects in the foreground will be highlighted by using edge detection. The support vector machine in the preprocessed image to know where the bone has fractured and where the bone was not fractured

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Identification of Abnormal Red Blood Cells and Diagnosing Specific Types of Anemia Using Image Processing and Support Vector Machine

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) ◽

10.1109/hnicem48295.2019.9072904 ◽

2019 ◽

Author(s):

Carlos C. Hortinela ◽

Jessie R. Balbin ◽

Janette C. Fausto ◽

Paul Daniel C. Divina ◽

John Philip T. Felices

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Red Blood Cells ◽

Blood Cells ◽

Support Vector

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Development of Abnormal Red Blood Cells Classifier Using Image Processing Techniques with Support Vector Machine

Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology ◽

10.1145/3397391.3397406 ◽

2020 ◽

Author(s):

Carlos C. Hortinela ◽

Janette C. Fausto ◽

Flordeliza L. Valiente ◽

Paul Daniel C. Divina ◽

John Philip T. Felices

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Red Blood Cells ◽

Blood Cells ◽

Support Vector ◽

Image Processing Techniques ◽

Processing Techniques

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Lung Cancer Detection using Image Processing Techniques and its Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38245 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1578-1583

Author(s):

Aishwarya .R

Keyword(s):

Lung Cancer ◽

Image Processing ◽

Support Vector Machine ◽

Cell Carcinoma ◽

Mortality Rates ◽

The Body ◽

Support Vector ◽

Lung Tumour ◽

Image Processing Techniques ◽

Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

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Detecting Edge Using Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.974 ◽

2012 ◽

Vol 588-589 ◽

pp. 974-977 ◽

Cited By ~ 1

Author(s):

Jih Pin Yeh

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Edge Detection ◽

Supervised Learning ◽

Classical Method ◽

Support Vector ◽

Learning Method ◽

Edge Detector

The edge detection is used in many applications in image processing. It is currently crucial technique of image processing. There are various methods for promoting edge detection. Here, it is presented that edge detection can be achieved using Support Vector Machine (SVM). Supervised learning method is applied. Laplacian edge detector is an instructor of Support Vector Machine. In this research, it is presented that any classical method can be applied for training of SVM as edge detector.

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Sickle cell segmentation and classification for thalassemia aid diagnosis

F1000Research ◽

10.12688/f1000research.73314.1 ◽

2021 ◽

Vol 10 ◽

pp. 1185

Author(s):

Yen-Siang Leow ◽

Kok-Why Ng ◽

Yih-Jian Yoong ◽

Seng-Beng Ng

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Decision Tree ◽

Complete Blood Count ◽

Similarity Index ◽

Random Forest Classifier ◽

The Body ◽

Cell Segmentation ◽

Support Vector ◽

Morphological Operations

Background: Thalassemia is a hereditary blood disease in which abnormal red blood cells (RBCs) carry insufficient oxygen throughout the body. Conventional methods of thalassemia detection through a complete blood count (CBC) test and peripheral blood smear image still possess a lot of weaknesses. Methods: This paper proposes a hybrid segmentation method to segment the RBCs. It incorporates adaptive thresholding and canny edge method to segment the RBCs. Morphological operations are performed to clean the leftovers. Shape and texture features are extracted using the segmented masks and the gray level co-occurrence matrix. Data imbalance treatment is used for solving the imbalance cell type class in distribution. In the data resampling layer, the synthetic minority oversampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and random over sampling (ROS) are performed and evaluated using the decision tree and logistic regression. In the classification layer, the decision tree, random forest classifier and support vector machine (SVM) are assessed and compared for the best performance in classification. Results:The proposed method outperforms the other methods in the image segmentation layer with the structural similarity index measure (SSIM) of 89.88%. In the data resampling layer, ADASYN is employed as it is more accurate than the SMOTE and ROS. The random forest classifier is chosen at the classification layer as it is more accurate than the decision tree and support vector machine (SVM). Conclusions:The proposed method is tested on the latest dataset of erythrocyteIDB3 and it solves the issues of imbalanced data due to the insufficient cell classes.

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