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

Pneumonia and tuberculosis detection and classification using deep learning in resource limited settings, Ethiopia

Analysis of CNN-Based Deep Learning Architectures and Performance Enhancement Strategies for Pneumonia Classification Using Chest X-Ray Images

Background: Deep learning models, particularly convolutional neural networks (CNNs), have shown promising performance for pneumonia detection using chest X-ray images. However, the impact of preprocessing, architecture selection, data augmentation, and ensemble strategies has not been systematically evaluated. This study investigated how these factors affect model robustness and diagnostic performance. Methods: A public pediatric chest X-ray dataset was used to systematically evaluate pixel normalization methods, six CNN architectures, progressive data augmentation strategies for class imbalance, and both feature-level and decision-level ensemble approaches. Model performance was assessed by considering not only overall classification accuracy but also clinically relevant risk metrics, particularly false-negative rates. Results: Pixel normalization to the 0–1 range improved model convergence, while Xception and InceptionV3 achieved the best overall performance. Model-specific augmentation strategies were more effective than a fixed 1:1 class ratio for reducing false negatives. Feature-level ensembles tended to overfit, whereas decision-level ensembles provided more stable but only modest performance improvements. Conclusions: These findings demonstrate that reliable medical AI systems require systematic optimization of preprocessing techniques, model architecture, data augmentation strategies, and clinically meaningful evaluation metrics rather than maximizing a single performance indicator. The proposed framework provides practical guidelines for developing robust deep learning models for pneumonia diagnosis in clinical settings.

A Deep Learning Approach for Multiclass Pneumonia Detection in Chest X-Ray Images

Pneumonia is a significant cause of mortality, particularly in children under five. Accurate detection of pneumonia from Chest X-ray (CXR) images is crucial in mitigating diagnostic errors common in manual radiographic analysis. This study leverages deep learning models to enhance the detection of multiclass pneumonia (normal, bacterial, and viral) using CXR images. We utilized a dataset comprising 5,863 multiclass pneumonia CXR samples. Data augmentation and regularization techniques were applied to address class imbalance and overfitting. Pre-trained models, including EfficientNet, MobileNet, RegNet, and ViT, were fine-tuned using the PyTorch framework, with transfer learning employed to optimize training. Model performance was assessed using accuracy, precision, recall, and specificity. The fine-tuned models achieved high classification accuracy, with EfficientNet and ConvNext models achieving accuracy scores of 83% and 82%, respectively. Data augmentation and regularization significantly improved the models' generalization, reducing overfitting and improving predictive accuracy. The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXRs. These models have the potential to support healthcare professionals in making more accurate diagnoses.

FPGA-Based ResNet-50 Acceleration for Automated Pneumonia Detection from Chest X-Ray Images

Pneumonia remains a leading cause of morbidity and mortality worldwide, and timely radiological screening is essential for effective treatment, particularly in settings where specialist radiologists are scarce. Convolutional neural networks (CNNs) classify chest X-ray images as normal or pneumonic with high accuracy, but their computational cost limits deployment on portable, low-power clinical devices. This paper presents an end-to-end pipeline that trains a ResNet-50 network, using transfer learning, to classify chest radiographs and deploys the trained model on a Xilinx ZCU104 Zynq UltraScale+ FPGA board through the Vitis AI toolchain. Images from a public pediatric pneumonia dataset (5216 training, 624 test, 16 validation images) are resized, normalized, and evaluated under several classical filtering operations before being passed to the network, which combines residual convolutional blocks, ReLU activations, and a sigmoid output layer. The model is trained for 30 epochs with the Adam optimizer and binary cross-entropy loss, reaching a validation accuracy in the 90–92% range. After quantization and compilation for the on-board Deep-Learning Processing Unit, the FPGA implementation achieves an inference throughput of 1250 frames per second at 4.1 W, compared with 44 frames per second at 19.9 W on a general-purpose CPU baseline — approximately a 28-fold gain in throughput and a 138-fold gain in energy efficiency. These results indicate that SoC-FPGA platforms are a practical route to real-time, energy-efficient pneumonia screening at the point of care.

Lung Disease Diagnosis Based on MRI Data Using CNN Transfer Learning Method with MobileNetV2 and DenseNet121

Accurate lung disease diagnosis plays a critical role in medical treatment and improving patient outcomes. Conventional diagnostic methods often require experienced radiologists and are time-consuming. This study develops a lung disease diagnosis system based on Magnetic Resonance Imaging (MRI) data using the Convolutional Neural Network (CNN) method with Transfer Learning, specifically utilizing MobileNetV2 and DenseNet121 architectures. The dataset comprises 7,141 MRI images collected from Kaggle and RS Islam Aysha Bogor, classified into nine categories: Bacterial Pneumonia, Covid-19, Normal, Tuberculosis, Pneumothorax, Viral Infection, Asthma, Bronchitis, and Bronchopneumonia. Images were preprocessed to 224×224 pixels with pixel normalization to [0,1]. Four experimental scenarios were evaluated, varying optimizer, learning rate, batch size, and number of epochs. Results showed that MobileNetV2 achieved the best accuracy of 92.14%using RMSprop optimizer, learning rate 0.001, batch size 32, and 40 epochs. DenseNet121 achieved 87.82% accuracy with Adam optimizer under the same configuration. Validation using the confusion matrix yielded an overall accuracy of 91%, precision of 91.37%, recall of 92.25%, and F1-score of 91.79%. The best model was deployed as a web-based application built with Python Flask, enabling automatic image normalization and real-time classification without manual preprocessing. This research demonstrates that CNN-based Transfer Learning is effective for automated lung disease diagnosis with limited datasets

An intelligent screening model for pneumonia in chest x-ray images based on residual networks and transfer learning

Pneumonia screening from chest X-ray (CXR) images is clinically important but often suffers from limited data and class imbalance in public benchmarks. This work develops a transfer-learning-based binary classifier using ResNet50 pretrained on ImageNet. Images are resized to 224×224 and normalized to [0,1]. Online augmentation (rotation, zoom, and shifts) is applied during training. To mitigate imbalance, class weights computed from the training labels are used in the loss. We fine-tune the network by freezing the early layers and updating higher-level layers with a small learning rate (Adam, 1e- 4) and early stopping. On the held-out test set (624 images), the model achieves about 0.88 accuracy. For the pneumonia class, precision, recall, and F1-score reach approximately 0.93, 0.88, and 0.90, respectively. The results indicate that combining class-weighted training with fine-tuned ResNet50 provides a practical baseline for automated pneumonia screening on CXR images.