Classification of Malignant and Benign Micro Calcifications from Mammogram Using Optimized Cascading Classifier

Krishnamoorthy, N.; Ramasamy, Asokan; Jones, Isabella

doi:10.2174/1574362411666160614083720

Cited by 12 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specificity, execution time and false positive rate were considered for performance analysis. Hence, experimental results shown, specificity values are 0.992, execution time has 1 s per slice and false positive rate has 0.075 respectively (Krishnamoorthy et al, 2016).…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan¹,

Ramasamy²

2021

Concurrent Engineering

View full text Add to dashboard Cite

Nowadays, the most demanding and time consuming task in medical image processing is Brain tumor segmentation and detection. Magnetic Resonance Imaging (MRI) is employed for creating a picture of any part in a body. MRI provides a competent quick manner for analyzing tumor in the brain. This proposed framework contains different stages for classifying tumor like Preprocessing, Feature extraction, Classification, and Segmentation. Initially, T1-weighted magnetic resonance brain images are considered as an input for computational purpose. Median filter is proposed to optimize the skull stripping in MRI images. Abnormal brain tissues are extracted in low contrast, in addition to meticulous location of edges of affected tissue can be detected. Then, Discrete Wavelet Transform (DWT) and Histogram of Oriented Gradients (HOG) are performing feature extraction process. HOG is used for extracting the features like texture and shape. Then, Classification is performed through Machine learning categorization techniques via Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). These classifiers classify the brain image as either normal or abnormal and the performance is analyzed by various parameters such as sensitivity, specificity and accuracy.

show abstract

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan¹,

Ramasamy²

2021

Concurrent Engineering

View full text Add to dashboard Cite

show abstract

“…Therefore, it has been widely used in real-time computer vision applications such as face detection [18], vehicle detection [19], pedestrian detection [20] and football detection in robotic soccer competitions [21]. The cascade classifier was also used in breast cancer detection in mammograms [22].…”

Section: Model Training For the Cascade Classifiermentioning

confidence: 99%

Real-time registration of 3D echo to x-ray fluoroscopy based on cascading classifiers and image registration

Ma¹,

Housden²,

Fazili³

et al. 2021

Phys. Med. Biol.

View full text Add to dashboard Cite

Three-dimensional (3D) transesophageal echocardiography (TEE) is one of the most significant advances in cardiac imaging. Although TEE provides real-time 3D visualization of heart tissues and blood vessels and has no ionizing radiation, x-ray fluoroscopy still dominates in guidance of cardiac interventions due to TEE having a limited field of view and poor visualization of surgical instruments. Therefore, fusing 3D echo with live x-ray images can provide a better guidance solution. This paper proposes a novel framework for image fusion by detecting the pose of the TEE probe in x-ray images in real-time. The framework does not require any manual initialization. Instead it uses a cascade classifier to compute the position and in-plane rotation angle of the TEE probe. The remaining degrees of freedom are determined by fast marching against a template library. The proposed framework is validated on phantoms and patient data. The target registration error for the phantom was 2.1 mm. In addition, 10 patient datasets, seven of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures, were used to test the clinical feasibility as well as accuracy. A mean registration error of 2.6 mm was achieved, which is well within typical clinical requirements.

show abstract

“…The third layer is an 11-layer convolution with no non-linearity. The researchers found that if ReLU is repeated, deep networks only have the capability of a linear classifier in the non-zero volume output domain [3]. A residual connection is also a crucial component of the bottleneck residual block.…”

Section: Introductionmentioning

confidence: 99%

Scalp disease analysis using deep learning models

Krishnamoorthy,

Jayanthi,

Kumaravel

et al. 2023

ACE

View full text Add to dashboard Cite

Alopecia areata, folliculitis, hair loss, and dandruff are common scalp hair disorders caused by nutritional imbalances, stress, and pollution in the environment. Specialized treatments, such as hair physiotherapy, have arisen to address this issue. This paper pro-poses a deep learning model that can analysis the scalp diseases with high accuracy, using this model we can predict the scalp diseases which makes easier for them to treat the dis-ease with good treatment using the mobile phones we can predict the diseases simply us-ing its image. This paper deals with an advanced classification model which predicts scalp disease. More than 1100 images are used as training dataset, CNN is used for the classifi-cation and the high accuracy is achieved using VGG16, VGG19 and MobileNetV2.

show abstract

Classification of Malignant and Benign Micro Calcifications from Mammogram Using Optimized Cascading Classifier

Cited by 12 publications

References 0 publications

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Real-time registration of 3D echo to x-ray fluoroscopy based on cascading classifiers and image registration

Scalp disease analysis using deep learning models

Contact Info

Product

Resources

About