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Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

Introduction:

Medical images are teeming with many features that can be considered for inspection. Generally, many processes in Computer-Aided System (CAD), such as pre-processing, isolating Regions of Interest (ROIs), and feature extracting process, can help to get the accurate classification of the diseases [1]. There are various approaches for highlighting ROIs, extracting the salient features, and suppressing the associated noises. Rule-based techniques have limited performance, and to improve efficiency, they are usually consolidated. Traditional approaches focused on fetching geometric or handcrafted features are generally treated to reduce dimensionality, elapsed time, and redundancy features concerning extract salient features.

Abstract:

The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network’s connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, GreyWolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA C MLP) is also compared with other classifiers, based on (SS C MLP), (GWO C MLP), and (GA C MLP), in performance metrics.

Existing work:

Previous research using X-ray images, a CNN model trained from scratch was suggested. The model’s extracted features fed K-NN, SVM, and decision tree in their model. And A weakly-supervised CNN model was proposed. A Manta-Ray Foraging Optimization technique, using differential evolution, was developed for feature selection. The authors evaluated their method by testing two COVID-19 x-ray datasets.

Disadvantage:

However, there is still a need for more improvements in the feature extraction and classification stages.

Proposed work:

This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network’s connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases.

Advantage:

  • High effectiveness

Algorithms:

Advanced Squirrel Search Optimization Algorithm (ASSOA), CNN, ResNet-

50.

System requirements:

  Software requirements:

  • Operating system           :   Windows.
  • Coding Language :   Python.

Hardware components:

System                 :   Pentium IV 2.4 GHz or intel

Hard Disk                      :   40 GB.

Floppy Drive       :   1.44 Mb.

Mouse                  :   Optical Mouse.

Ram                      :   512 Mb.

Conclusion:

Developing a classification model for diagnosing infected cases is considered one of the most critical problems, which is still much too pricey for the mass selection. This paper proposes a classification model to detect the affected instances from the chest X-ray images, which may dramatically minimize the diagnosis prices, particularly in cultivating nations. The training and feature extraction processes are based on a convolutional neural network (CNN) based model (ResNet50) with _ne-tuning and image augmentation. The X-ray images’ classification to viral, normal, and bacterial, and popular scenarios are based upon an MLP neural network along with the proposed ASSOA algorithm. In this work, the chest X-ray images (Pneumonia) dataset composed of 5,863 X-ray images are utilized in the experiments. In the proposed model, a transfer learning technique is applied during the training stage and feature extraction. Experimental results show the proposed classification model’s efficiency in classifying the affected situations and a mean accuracy of (99.26%), which surpasses the cutting edge strategies discovered in the literature. The proposed (ASSOA C MLP) algorithm also achieved a classification mean accuracy of (99.7%) for another chest X-ray COVID-19 dataset.

March 19, 2022

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