Applying machine learning classification techniques for disease diagnosis from medical imaging data using Transformer based Attention Guided CNN (TAGCNN)

This research presents an innovative approach to disease diagnosis from medical imaging data, specifically focusing on X-ray images of osteoporosis. The proposed method revolves around a Transformer-based Guided Convolutional Neural Network (TGCNN), which integrates the spatial awareness of CNNs with the sophisticated relationship modeling capabilities of Transformers. The model is meticulously trained, achieving a training accuracy of 96% and a testing accuracy of 92%, with corresponding training and testing losses of 0.0034 and 0.0364, respectively. To gauge its effectiveness, TGCNN is rigorously compared with established models, namely ResNet50, AlexNet, and DenseNet169, showcasing superior performance. ResNet50 achieves a training accuracy of 86% and testing accuracy of 82%, with losses of 0.064 and 0.174. AlexNet demonstrates a training accuracy of 83% and testing accuracy of 78%, with losses of 0.04 and 0.64. DenseNet169 attains a training accuracy of 92%, testing accuracy of 88%, and losses of 0.0674 and 0.094. The comprehensive results and comparative analysis affirm the efficacy of the proposed TGCNN model for accurate and efficient disease diagnosis from X-ray images. This work sets the stage for further advancements in medical imaging diagnostics, with potential applications in diverse datasets and real-time diagnostic scenarios. Future work will explore optimizations and extensions to enhance the model’s versatility and performance across various medical imaging domains.