# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
## 2 The Literature Review  
[PERSON] and [PERSON] [11] introduced a classification method based on deep learning to identify lung diseases from chest X-ray images, with a specific emphasis on detecting COVID-19. This approach aims to categorize chest X-ray images into three groups: normal; COVID-19; and viral pneumonia. It employs an eight-layer convolutional neural network that combines MobileNet [12] and DenseNet [13] models. The research findings indicate a precision value of 1.00 for COVID-19 and normal cases while achieving a precision of 0.79 for viral pneumonia. The recall values are 1.00 for normal and viral pneumonia and 0.69 for COVID-19. The F1 score is found to be 1.00 for normal, 0.79 for COVID-19, and 0.85 for viral pneumonia. The proposed method achieves an impressive accuracy of 96% and a ROC AUC score of 0.94. These outcomes showcase the remarkable accuracy of the proposed approach in diagnosing and classifying chest X-ray images, surpassing the performance of traditional CNN and MobileNet methods. The method's high precision and F1 score are particularly important for minimizing false negatives, thereby aiding in the prevention of disease transmission. [PERSON] et al. [14] presented a method that uses deep-learning models, pre-processing techniques, and lung segmentation to improve the precision of COVID-19 detection in chest X-ray images. The study uses InceptionV3 [15] and U-Net [16], which are deep-learning models, to process and identify chest X-ray images as either COVID-19-negative or positive. By adding lung segmentation during pre-processing, this study aims to remove irrelevant surrounding information that could introduce bias and create inaccurate results. The results of this study show an amazing accuracy rate of approximately 99% for the most effective models in spotting COVID-19. However, this study also shows the effect of visual noise on model bias and underscores the value of lung segmentation in reducing bias and ensuring more consistent results. This study admits that the current models strongly rely on visible abnormalities in the lungs as signs of COVID-19, and further improvements are necessary to address this weakness. [PERSON] et al. [17] conducted a study utilizing the Inception ResNet-v2 [18] architecture and transfer learning to classify chest X-ray images into three categories: normal; viral pneumonia; and COVID-19. The dataset consisted of 3616 COVID-19 cases, 10,192 of normal cases, and 1345 cases of viral pneumonia. The model underwent two rounds of training. Initially, the Inception ResNet-v2 layers were frozen, preserving the weights and biases learned from the ImageNet dataset. Only the additional layers were added after the Inception ResNet-v2 was trained. In the second training phase, all layers were unfrozen, allowing for further fine-tuning of the entire model. This research highlighted that the model performed better in classifying images belonging to viral pneumonia and normal classes compared to the COVID-19 class, as indicated by higher precision, recall, and F1 scores. The overall accuracy of the model was reported to be 0.966, with an F1 score of 0.97. These findings demonstrate the potential of using the Inception ResNet-v2 architecture and transfer learning for accurate classification of chest X-ray images, particularly in distinguishing between viral pneumonia, normal, and COVID-19 cases, contributing to advancements in medical imaging and healthcare applications. [PERSON] et al. [19] conducted a study to investigate the application of transfer learning using the DeTraC (Decompose, Transfer, and Compose) deep CNN architecture for COVID-19 chest X-ray classification. DeTraC incorporates a class decomposition mechanism to address irregularities presented in the image dataset. This study demonstrates the effectiveness of DeTraC in accurately classifying COVID-19 cases while also showcasing its robustness in handling data irregularities and the limited availability of training images.