Gastrointestinal Disease Detection using deep learning architectures
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Gastrointestinal disease detection is an important task in medical image analysis and the Hyper-Kvasir dataset is a widely-used benchmark dataset for this task. Various deep learning architectures are implemented and comparative analysis is performed to obtain best model for detection. ...learn more
Project status: Published/In Market
Overview / Usage
Various deep learning architectures are trained in order to perform the task of gastrointestinal disease detection. The Hyper-Kvasir dataset is a widely-used benchmark dataset for this task. A comparative analysis is performed between various deep learning architectures.
Methodology / Approach
Started with data preprocessing, including resizing images and data augmentation, followed by fine-tuning the pre-trained ResNet50 model and training the model using the Adam optimizer. The model's performance was evaluated on the testing set, and the results showed an accuracy of around 92% after five epochs. Overall, the implementation of ResNet50 on the hyper-kvasir dataset shows promising results in detecting gastrointestinal diseases.
Technologies Used
Deep Learning, TensorFlow, pandas, NumPy, Python