Red Blood Cell Classification: Deep Learning Architecture Versus Support Vector Machine

2018 ◽  
Author(s):  
Hajara Abdulkarim Aliyu ◽  
Rubita Sudirman ◽  
Mohd Azhar Abdul Razak ◽  
Muhamad Amin Abd Wahab
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Blood Cell ◽  
Red Blood Cell ◽  
Support Vector ◽  
Cell Classification

scholarly journals Leukocyte (White Blood Cell) Classification with a Multi-stage Support Vector Machine

2020 ◽  
pp. 216-224
Author(s):  
Hoang Truong Kien ◽  
Nguyen Hoai Phuong ◽  
Hoang Thi Luyen ◽  
Nguyen Minh Duc ◽  
Duong Trong Luong
Keyword(s):  
Support Vector Machine ◽  
Blood Cell ◽  
White Blood Cell ◽  
Support Vector ◽  
Cell Classification ◽  
Multi Stage

Deep learning model for protein disease classification

2021 ◽  
Vol 16 ◽  
Author(s):  
Farida Alaaeldin Mostafa ◽  
Yasmine Mohamed Afify ◽  
Rasha Mohamed Ismail ◽  
Nagwa Lotfy Badr
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Protein Analysis ◽  
Kernel Functions ◽  
Support Vector ◽  
Feature Descriptors

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.

scholarly journals Red blood cell classification in lensless single random phase encoding using convolutional neural networks

2020 ◽  
Vol 28 (22) ◽  
pp. 33504 ◽  
Author(s):  
Timothy O’Connor ◽  
Christopher Hawxhurst ◽  
Leslie M. Shor ◽  
Bahram Javidi
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Random Phase ◽  
Cell Classification ◽  
Phase Encoding ◽  
Random Phase Encoding

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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