Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images

Ramya, J.; Rajakumar, M. P.; Maheswari, B. Uma

doi:10.1007/s10278-021-00534-2

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…e feature presented in (9) is then considered to train and test the classifiers considered in this study. e various binary classifiers considered in this research include Soft-Max, Naïve-Bayes (NB), random forest (RF), decision tree (DT) variants, K-nearest neighbors (KNN) variants, and SVM with linear kernel [40][41][42][43].…”

Section: Feature Reduction With Spotted Hyena Algorithmmentioning

confidence: 99%

Tuberculosis Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features

Kadry

Srivastava

Rajinikanth

et al. 2022

Computational Intelligence and Neuroscience

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Lung abnormality in humans is steadily increasing due to various causes, and early recognition and treatment are extensively suggested. Tuberculosis (TB) is one of the lung diseases, and due to its occurrence rate and harshness, the World Health Organization (WHO) lists TB among the top ten diseases which lead to death. The clinical level detection of TB is usually performed using bio-medical imaging methods, and a chest X-ray is a commonly adopted imaging modality. This work aims to develop an automated procedure to detect TB from X-ray images using VGG-UNet-supported joint segmentation and classification. The various phases of the proposed scheme involved; (i) image collection and resizing, (ii) deep-features mining, (iii) segmentation of lung section, (iv) local-binary-pattern (LBP) generation and feature extraction, (v) optimal feature selection using spotted hyena algorithm (SHA), (vi) serial feature concatenation, and (vii) classification and validation. This research considered 3000 test images (1500 healthy and 1500 TB class) for the assessment, and the proposed experiment is implemented using Matlab®. This work implements the pretrained models to detect TB in X-rays with improved accuracy, and this research helped achieve a classification accuracy of >99% with a fine-tree classifier.

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Section: Feature Reduction With Spotted Hyena Algorithmmentioning

confidence: 99%

Tuberculosis Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features

Kadry

Srivastava

Rajinikanth

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…In the CNN models, numerous hyperparameters, such as the number and size of convolutional kernels in each convolutional layer, the dropout size, activation function types, etc., must be determined manually However, to achieve an excellent network structure, these hyperparameters should not be selected manually based on personal experience alone (Ramya et al , 2022; Kim et al , 2021; Mantang et al , 2020; Pandey and Janghel, 2021). CNNs with artificially determined hyperparameters are likely to fail in achieving the desired performance after going through multiple training sessions, and artificially modified hyperparameters can not necessarily improve the performance of CNNs, causing not only a waste of computational resources but also creating obstacles in obtaining the ideal network structure.…”

Section: Introductionmentioning

confidence: 99%

Music sentiment classification based on an optimized CNN-RF-QPSO model

Tian

Yin

Gan

2023

DTA

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PurposeMusic sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.Design/methodology/approachA CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.FindingsThe model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.Originality/valueThe dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

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Integrating DCNNs with Genetic Algorithm for Diabetic Retinopathy Classification

Li,

Wu,

2023

Big Data

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Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images

Cited by 6 publications

References 40 publications

Tuberculosis Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features

Tuberculosis Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features

Music sentiment classification based on an optimized CNN-RF-QPSO model

Integrating DCNNs with Genetic Algorithm for Diabetic Retinopathy Classification

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