Intelligent Method for COVID-19 Diagnosis: Construction and Comparative Analysis of ResKAN18

Comparison Of CNN, Resnet 50, And Vgg 16 For Pneumonia Classification Using Transfer Learning

The results showed that residual learning on ResNet50 effectively addressed the vanishing gradient problem in deep networks and achieved clinically relevant classification accuracy, supporting its potential integration into computer-aided diagnosis (CAD) systems.

Hybrid Ensemble Learning for Multi-Class Chest X-Ray Classification Using Deep CNN

Background: COVID-19, pneumonia, and TB (tuberculosis) are still the big killers of people suffering from chest disease and continue to be a serious health challenge globally. A timely diagnosis leads to timely treatment and improved patient outcomes. Chest X-ray (CXR) imaging is widely used for diagnostic purposes due to its speed, low cost, and availability in most healthcare facilities. Manual reading of CXR images is, however, challenging because of the similarity in the presentation of radiographic features across various chest diseases. Methods: This research introduces a hybrid ensemble learning approach to classify chest X-ray images into four classes—Normal, COVID-19, Pneumonia, and Tuberculosis. Three Deep CNN network models, namely Xception, AlexNet, and EfficientNet-B0, were used for deep feature extraction. Additionally, texture features of the images were extracted using Gabor filters. The deep and texture features were combined and classified using logistic regression and a stacking ensemble learning approach. A publicly available chest X-ray image database containing 7,135 X-rays was used, with six-fold stratified cross-validation to assess the proposed approach. Results: The ensemble models outperformed the individual CNN models. The Average Ensemble produced the best results with an accuracy of 91.18%, an Average Precision (AP) of 96.75%, and an Area Under the ROC Curve (AUC) of 98.87%. The proposed model performs well across all four disease classes. It exhibited high sensitivity in detecting Tuberculosis with considerable stability in classifying Normal, COVID-19, and Pneumonia. Conclusion: The proposed framework demonstrates the effectiveness of integrating deep learning features, Gabor texture features, and ensemble learning for improved chest X-ray image classification. This can help computer-assisted diagnostics systems and aid medical workers in identifying chest diseases early.

COVID-19 Chest X-ray Image Classification: A Comparative Research of LBP-SVM and MobileNetV2

: Since the COVID-19 outbreak, Reverse Transcription Polymerase Chain Re-action (RT-PCR) has limitations of long cycles and low sensitivity, making medical imaging (CT/X-ray) critical for early auxiliary diagnosis. However, manual image interpretation by radiologists is inefficient and prone to errors due to subjective factors.To address this, this paper compares two automated solutions using the CoronaHack dataset with preprocessing: LBP-based SVM and transfer learning-enabled MobileNetV2. Experimental results indicated that the optimized LBP-SVM model attained a test accuracy of 80.65%, whereas the MobileNetV2 model achieved a test accuracy of 95.36% and a recall rate of 98.40 — with only 4 out of 250 true positive samples being missed. Because the application of data augmentation and regularization techniques, the training and validation loss curves of MobileNetV2 con-verged effectively, which in turn suppressed the issue of overfitting. This confirms the superiority of MobileNetV2 in end-to-end feature extraction for COVID-19 imaging.

Detection and Classification of Colorectal Cancer Types Using Deep Residual Learning based on ResNet-50 with Adam Optimization Method

Revisiting Channel and Spatial Attention in ResNet50 for Chest X-ray Classification

Attention mechanisms are widely adopted in convolutional neural networks to improve feature representation, yet their consistent effectiveness in medical image classification remains uncertain. This study systematically evaluates channel and spatial attention mechanisms integrated into a ResNet50 architecture for chest X-ray classification tasks. Three attention configurations are examined, namely Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and a hybrid combination of both, using three publicly available chest X-ray datasets: Pneumonia Chest X-ray, COVID-QU-Ex, and Tuberculosis Chest X-ray. All model variants are trained under identical preprocessing and optimization settings to ensure a controlled and fair comparison. Each experiment is repeated across multiple random seeds to reduce the influence of stochastic variability, and final performance is reported as the mean across runs. Model effectiveness is assessed using accuracy, precision, recall, and F1 score, followed by paired t-tests to determine the statistical significance of observed differences. Quantitative results indicate that attention-augmented models produce marginal performance changes relative to the ResNet50 baseline across all three datasets. Statistical testing confirms that none of the observed improvements reach significance, with all p-values exceeding 0.05. These findings suggest that attention mechanisms do not function as universally effective performance boosters in chest X-ray classification. Instead, their role appears to be supportive in nature, contributing to feature representation stability rather than driving substantial predictive gains. Dataset characteristics and attention placement are identified as key factors influencing the degree of benefit observed.

A deep learning framework for COVID-19 detection: integrating attention modules into ResNet50 and optimizing cross-entropy loss function

Rapid and reliable diagnosis of COVID-19 is of fundamental importance for effective pandemic control, and chest X-ray imaging combined with deep learning provides a very useful tool for large-scale screening. Therefore, this paper presents a tri-class classification framework for distinguishing COVID-19, viral pneumonia, and normal cases from chest X-rays. The method is built on a pre-trained ResNet50 and incorporates CBAM attention modules at two well-justified stages: spatial attention in Layer3 for abnormal region localization and channel attention in Layer4 for semantic feature selection. More importantly, it addresses the class imbalance common in medical data by using a weighted cross-entropy loss function. Experiments on a public COVID-19 chest X-ray dataset demonstrate that the full model attains 90.74% precision and 74.65% F1-score, both superior to baseline methods. Ablation studies rigorously validate each component, and Precision-Recall curve analysis gives an AUC-PR of 0.942. From the results for the COVID-19 class it is clearly seen that the attention map visualization shows where the model is looking at clinically relevant lung regions, and since the inference time is 2.64 ms per image (379.1 FPS), the proposed framework thus achieves a good balance between accuracy and speed for clinical use.