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

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

Lung Cancer Detection using Image Processing Techniques and its Classification

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;

scholarly journals An Efficient and Robust Fracture Detection in Femur Bones

2019 ◽  
Vol 9 (1) ◽  
pp. 1954-1957 ◽  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Edge Detection ◽  
Red Blood Cells ◽  
Blood Cells ◽  
The Body ◽  
Support Vector ◽  
Morphological Operations ◽  
Fracture Detection

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

scholarly journals CAD System for Lung Cancer and its Stages Detection using Image Processing Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1612-1615
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
False Positive ◽  
False Negative ◽  
Region Growing ◽  
High Rate ◽  
Support Vector ◽  
Image Region ◽  
Image Processing Techniques ◽  
Processing Techniques

The Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system

scholarly journals Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

2020 ◽  
Vol 9 (7S) ◽  
pp. 38-42
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Expert Advice ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Image Processing Techniques ◽  
Processing Techniques

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

Mathematical models for some problems in textile engineering and its corresponding discussions

2014 ◽  
Vol 05 (02) ◽  
pp. 1350030
Author(s):  
Li Zhang ◽  
Xinhua You ◽  
Jun Chen ◽  
Liang Zhang
Keyword(s):  
Image Processing ◽  
Cluster Analysis ◽  
Textile Industry ◽  
Programming Algorithm ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Image Processing Techniques ◽  
Chinese Industry ◽  
Processing Techniques

Textile industry is very important to the development of Chinese industry and economy. Image processing techniques are beneficial to improve the quality of cotton goods. Suitable blending ratio of yarn is good for it. It is significant to measure the blending ratio of yarn in the practice of textile engineering. Combined with results done by other scholars, this paper uses the concepts of acreage index, abnormity index and fluctuation index. Based on these morphologic indices, it is convenient to construct corresponding eigenvector and to discuss useful mathematical method for the cluster analysis during the measurement of the blending ratio. This paper also sets up some kinds of nonlinear optimization model for the problem. Using classical integer programming algorithm, support vector machine algorithm and genetic algorithm to the problem, we get fine results for cluster analysis. Finally, we give out another problem of image processing and have some discussions about it.

scholarly journals Detection and Classification of Rice Diseases: An Automated Approach Using Textural Features

2019 ◽  
Vol 38 (1) ◽  
pp. 239-250 ◽  
Author(s):  
Komal Bashir ◽  
Mariam Rehman ◽  
Mehwish Bari
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Recall Rate ◽  
Misclassification Rate ◽  
Maximum Efficiency ◽  
Support Vector ◽  
Image Processing Techniques ◽  
Effective Decision Making ◽  
Processing Techniques

Image processing techniques are widely used for the detection and classification of diseases for various plants. The structure of the plant and appearance of the disease on the plant pose a challenge for image processing. This research implements SVM (Support Vector Machine) based image-processing approach to analyze and classify three of the rice crop diseases. The process consists of two phases, i.e. training phase and disease prediction phase. The approach identifies disease on the leaf using trained classifier. The proposed research work optimizes SVM parameters (gamma, nu) for maximum efficiency. The results show that the proposed approach achieved 94.16% accuracy with 5.83% misclassification rate, 91.6% recall rate and 90.9% precision. These findings were compared with image processing techniques discussed in review of literature. The results of comparison conclude that the proposed methodology yields high accuracy percentage as compared to the other techniques. The results obtained can help the development of an effective software solution by incorporating image processing and collaboration features. This may facilitate the farmers and other bodies in effective decision making to efficiently protect the rice crops from substantial damage. While considering the findings of this research, the presented technique may be considered as a potential solution for adding image processing techniques to KM (Knowledge Management) systems.

scholarly journals Fruit Quality Detection and Gradation

2021 ◽  
pp. 383-388
Author(s):  
Harshal S. Deshmukh ◽  
Dr. S. W. Mohod ◽  
Dr. N. N. Khalsa
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Support Vector ◽  
Color Classification ◽  
Image Processing Techniques ◽  
Background Removal ◽  
Quality Detection ◽  
Analysis Methods ◽  
Processing Techniques

Grading and classification of fruits is based on observations and through experiences. The system exerts image- processing techniques for classification and grading the quality of fruits. Two-dimensional fruit images are classified on shape and color-based analysis methods. However, different fruit images have different or same color and shape values. Hence, using color or shape analysis methods are still not that much effective enough to identify and distinguish fruits images. Therefore, computer vision and image processing techniques have been found increasingly useful in the food industry, especially for applications in quality detection. Research in this area indicates the feasibility of using computer vision systems to improve product quality, the use of computer vision for the inspection of food has increased during recent years. This proposed work presents food quality detection system. The system design considers some feature that includes fruit colors and size, which increases accuracy for detection of roots pixels. Histogram of oriented gradients is used for background removal, for color classification, support vector machine is used.

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