A Deep Learning Based Detection and Classification of Gastrointestinal Diseases

Detecting diseases and abnormalities in the gastrointestinal tract poses a significant challenge for healthcare professionals and can greatly impact treatment. Deep learning has become increasingly important in the analysis of medical images, helping with diagnosis, disease prediction, and treatment evaluation. Several research studies have utilized CNNs to accurately classify medical images. However, current techniques for identifying gastrointestinal abnormalities often utilize intricate architectures that necessitate extensive parameter tuning. This underscores the need for a streamlined deep learning approach that is easily replicable while maintaining the high accuracy of more intricate models. This research introduces a methodology for classifying gastrointestinal diseases and anomalies using image processing and a transfer learning technique. Leveraging deep learning methods, our objective is to develop an advanced and precise model for predicting diseases.