Effectiveness of Pre-Processing and Deep Learning Methods in Medical Imaging Diagnostics

Create graphical abstract for scientific articles Title: Effectiveness of Pre-Processing and Deep Learning Methods in Medical Imaging Diagnostics Main Objective: To explore the effectiveness of five pre-processing methods and five deep learning models for medical image diagnostics across diverse datasets, aiming to identify the most suitable combinations for different medical image types. Key Findings: 1. Best Pre-Processing Methods: Median-Mean Hybrid Filter and Unsharp Masking + Bilateral Filter (87.5% effectiveness) 2. Top Deep Learning Models: EfficientNet-B4 and MobileNetV2 (75% effectiveness) 3. Performance Insight: MobileNetV2 offers a significant reduction in runtime by 34% compared to other models. Methodology: • Pre-Processing Techniques: CLAHE + Butterworth, DWT + Threshold, CLAHE + Median Filter, Median-Mean Hybrid Filter, Unsharp Masking + Bilateral Filter • Deep Learning Models: EfficientNet-B4, ResNet-50, DenseNet-169, VGG16, MobileNetV2 • Datasets: o X-ray: COVID19 Pneumonia Normal Chest PA, Osteoporosis Knee X-ray o CT: Chest CT-Scan Images, Brain Stroke CT Image o MRI: Breast Cancer Patients MRI's, Brain Tumor MRI o Ultrasound: Ultrasound Breast Images for Breast Cancer, MT Small Dataset Conclusion: The study identifies the most effective pre-processing methods and deep learning models, providing insights for improved diagnostic accuracy and efficiency. Visual Elements: 1. Title at the top 2. Illustrations of the five pre-processing methods and five deep learning models in a grid format 3. Arrows or connections showing combinations tested 4. Highlight of the top-performing methods and models 5. Datasets represented with icons (X-ray, MRI, CT, Ultrasound) 6. Key performance metrics (effectiveness ratios and runtime improvement) displayed prominently