# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
## 2 The Literature Review
Through validation experiments with various pre-trained CNN models, VGG19 [20] emerged as the most successful model within the DeTraC framework. The experimental results highlight the impressive performance of DeTraC in detecting COVID-19 cases,achieving an accuracy of 93.1% with a sensitivity of 100% in accurately distinguishing COVID-19 X-ray images from both normal and severe acute respiratory syndrome cases. [PERSON] and [PERSON] [5] proposed a framework for detecting COVID-19 and pneumonia in chest X-ray images. The framework is divided into multiple steps, which include dataset gathering, picture quality improvement, ROI estimation, feature extraction, and illness classification. Two publicly accessible chest X-ray datasets are used, and picture quality is improved by utilizing such techniques as median filtering and histogram equalization. Various characteristics, such as visual, shape, texture, and intensity, are retrieved and normalized from each ROI picture. Soft computing approaches, such as ANN [21], SVM [22], KNN [23], ensemble classifiers [24], and a deep-learning classifier dubbed F-RNN-LSTM, are used for classification. The F-RNN-LSTM deep-learning architecture combines RNN and LSTM for enhanced disease categorization. Experiment findings show that the suggested framework is successful. When compared to previous approaches, the F-RNN-LSTM model achieves an accuracy of roughly 95% while requiring less computing effort. [PERSON] [25] introduced an innovative method for detecting COVID-19 from X-ray images by utilizing a deep LSTM model. The model is developed from scratch, offering a unique architecture specifically designed for this purpose. To enhance the model's performance, the study incorporates such pre-processing techniques as the Sobel gradient and marker-controlled watershed segmentation. This research conducts experiments on a combined public dataset consisting of 361 COVID-19, 500 pneumonia, and 200 normal chest X-ray images. The dataset is divided randomly into training and testing sets, with different ratios tested. The most favorable results are obtained when using an 80% training and 20% testing split. Impressively, the proposed model achieves a perfect 100% success rate across all performance metrics, including accuracy, sensitivity, specificity, and F-score. These findings are particularly remarkable considering the small size of the dataset used in the study. [PERSON] et al. [26] introduced the RCAL-BiLSTM model, which combines ResNet [27], a class attention layer (CAL) [28], and a Bi-LSTM. The model comprises several stages, including preprocessing, using bilateral filtering [29], feature extraction, using RCAL-BiLSTM, and classification employing SoftMax. Feature extraction involves ResNet for extracting features, CAL for capturing discriminative class-based features, and Bi-LSTM for modeling class dependencies in both directions. The SoftMax layer is then used to classify the feature vectors into their respective feature maps. Experimental validation is performed on a dataset of chest X-ray images, and the results illustrate the superior performance of the RCAL-BiLSTM model. It achieves high sensitivity (93.28%), specificity (94.61%), precision (94.90%), accuracy (94.88%), F-score (93.10%), and kappa value (91.40%), highlighting the effectiveness of the proposed model for COVID-19 diagnosis. [PERSON] et al. [30] proposed a CNN-LSTM architecture combined with an improved optimization algorithm to address the challenges of multisource fusion and redundant features. The dataset consisted of four classes: COVID-19; normal, viral pneumonia, and lung opacity. The framework includes contrast enhancement and data augmentation to improve the quality and quantity of training samples. Deep transfer learning is utilized in training a CNN-LSTM model and fine-tuning an EfficientNet [31] model for feature extraction. The overall accuracy achieved was 98.5%. [PERSON] et al.