scholarly journals Multiscale Optimization of Nanocomposites with Probabilistic Feature Descriptors

AIAA Journal ◽  
2018 ◽  
Vol 56 (7) ◽  
pp. 2936-2941 ◽  
Author(s):  
Pınar Acar ◽  
Veera Sundararaghavan ◽  
Nicholas Fasanella
Keyword(s):  
Multiscale Optimization ◽  
Feature Descriptors

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals An Evaluation of Traditional and CNN-based Feature Descriptors for Cartoon Pornography Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Nouar AlDahoul ◽  
Hezerul Abdul Karim ◽  
Mohd Haris Lye Abdullah ◽  
Abdulaziz Saleh Ba Wazir ◽  
Mohammad Faizal Ahmad Fauzi ◽  
...  
Keyword(s):  
Feature Descriptors

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

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