Revolutionizing the Classification of Medical Image Classification by using integrating advanced neural networks with pre-processing

Preprocessing and classifying medical images using deep learning is critical to modern healthcare and improves diagnostic processes’ accuracy and efficiency. Using case studies of the most common gastrointestinal diseases, this article examines preprocessing methods and the advanced efficientnet_b0 model. The dataset contains multiple class images of ulcerative colitis, normal appendix, normal pylorus, normal Z-line, stained raised polyps, and stained resection margins. Greyscale conversion, histogram equalization, contrast enhancement, and noise reduction were the first of several preprocessing steps used. With each step, the image quality improved, and the proposed model’s performance increased. Results: The proposed model achieved a 99% accuracy rate. The classification report also shows the effectiveness of the model with high recall, precision, and F1 scores in each category. Combined with ideal preprocessing, Efficientnet_b0 achieves the best multiple classification across different categories of gastrointestinal diseases, thus representing a valuable medical diagnostic methodology tool. The main point of the discussion is to show that the Efficientnet_b0 model is reliable at capturing the differences between medical images by preprocessing them to improve their features without making them too similar. This study introduces a new method in the field of medical image classification that combines preprocessing and artificial neural networks to achieve better results.