10th International Congress on Information and Communication Technology

Authors - Vasileios E. Papageorgiou, Dimitrios-Panagiotis Papageorgiou, Georgios Petmezas, Pan-telis Dogoulis, Nicos Maglaveras, George Tsaklidis
Abstract - This study presents a computationally efficient Convolutional Neural Network (CNN) enhanced with transfer learning for medical image classifica-tion. The method was rigorously tested on 3 tumor datasets: brain MRI, and lung and kidney CT scans. It leverages a pre-trained CNN on brain MRI images, fine-tuned with minimal re-training for the CT scans, achieving high classification accuracy. Transfer learning allows the model to adapt to cancer-specific features by utilizing insights from large datasets. Re-training on each tumor type using only 20 epochs, can deliver significant classification performance, demonstrating the method's efficiency. The CNN's computational efficiency ensures it is both accurate and scalable, making it suitable for use in resource-constrained environ-ments. This research highlights the potential of low-complexity deep learning (DL) to accelerate cancer diagnosis while balancing accuracy and efficiency. It shows that complex deep learning models are not always necessary, and optimal performance can be achieved with lower computational costs.