COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Abstract

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

Refining the Accuracy of Chest X-Ray Image Classification Through Layer and Activation Function Optimization

“…The performance of the established VGG-19 models and proposed CXR-DLM model are implemented using MATLAB programming. Figure 3 (24) CNN-LSTM and fusionoptimization Chest X-ray (Pneumonia-2599, COVID-19-1698, Tuberculosis-1538, Normal-1575)…”

MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images

“…Asari M.A et al [42] developed a pre-trained GoogleNet model-based automated system to diagnose COVID-19 from the CXRs with an accuracy of 91.0%. Ameer Hamza et al [43] proposed an optimal features generation by a CNN-LSTM model and are classified by the QSVM classifier with an accuracy of 93.4%. A modified MobileNetV2 is a lightweight CNN model introduced to detect COVID-19 from CXR images by T. Sanida et al [44] .…”

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust

“…Some researchers also applied heuristic-based methods for Covid-19 medical image analysis. Hamza et al [ 26 ] used moth flame optimization on a CNN-LSTM based Covid-19 classification model on chest X-rays. Chest X-rays were used to enhance the dataset with data augmentation and a CNN-LSTM model was implemented for deep feature extraction.…”