scholarly journals A new approach for breast abnormality detection based on thermography

2018 ◽  
Vol 2 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Chebbah Nabil Karim ◽  
Ouslim Mohamed ◽  
Temmar Ryad
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormality Detection ◽  
Public Database ◽  
New Approach ◽  
Image Processing Techniques ◽  
Breast Abnormality ◽  
Processing Techniques

Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method  showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.

scholarly journals Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ramin Nateghi ◽  
Habibollah Danyali ◽  
Mohammad Sadegh Helfroush ◽  
Ashkan Tashk
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
False Positives ◽  
Support Vector ◽  
Svm Classifier ◽  
Computer Assisted ◽  
Cad System ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Mitotic Cells

This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

scholarly journals Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 30-42
Author(s):  
Oladimeji Adeyemi ◽  
Martins Irhebhude ◽  
Adeola Kolawole
Keyword(s):  
Image Processing ◽  
Image Processing Technique ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Stage ◽  
Image Processing Techniques ◽  
Stop Sign ◽  
Processing Techniques ◽  
Road Marking ◽  
Detection And Recognition

This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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 Breast Thermograms Asymmetry Analysis using Gabor filters

2021 ◽  
Vol 309 ◽  
pp. 01109
Author(s):  
Priyanka Yadlapalli ◽  
Madhavi K Reddy ◽  
Sunitha Gurram ◽  
J Avanija ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Cost Effective ◽  
Gabor Filters ◽  
Support Vector ◽  
Svm Classifier ◽  
Infrared Thermal Imaging ◽  
Younger Women ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

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 Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1870
Author(s):  
Yaghoub Pourasad ◽  
Esmaeil Zarouri ◽  
Mohammad Salemizadeh Parizi ◽  
Amin Salih Mohammed
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
K Nearest Neighbor ◽  
Diagnose Breast Cancer ◽  
Premature Deaths ◽  
Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

Commutation Sparking Image Monitoring for DC Motor

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Fei Hu ◽  
Qingbo He ◽  
Jianping Wang ◽  
Zhigang Liu ◽  
Fanrang Kong
Keyword(s):  
Dc Motor ◽  
Characteristic Parameters ◽  
Image Preprocessing ◽  
New Approach ◽  
Image Processing Techniques ◽  
The Status ◽  
Image Monitoring ◽  
Motor Condition ◽  
Processing Techniques ◽  
The Relationship

As one of the most important parameters in evaluating the status of a DC motor, commutation spark has been widely used in condition monitoring and fault diagnosis. A new approach using image processing techniques on the commutation spark has been proposed. Advantageous over other methods using partial spark information, the details about the motor condition can be comprehensively retained and extracted from the sparking images. The sparking images were obtained by the cameras, which were fixed at the certain sites in DC motor beforehand. The images were processed through the sparking image preprocessing, segmentation, enhancement, and feature extraction. Comparing the characteristic parameters of the sparking image with the result of the subjective grading, the relationship between the parameters and the sparking grades had been analyzed to monitor the DC motor. The effectiveness had been demonstrated by application examples.

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