Elevating X-ray Image Classification Performance with wavelet Fusion and deep Learning
Keywords:
Selected:X-ray Image classification, CNN, wavelet transform, Image fusion, deep learningAbstract
Medical imaging has seen a significant advancement in recent years, with the introduction of multi-spectral X-ray imaging being one notable development. However, traditional image fusion methods such as weighted averaging or maximum intensity projection may not be sufficient to address the complexities of X-ray images. In response, a new deep learning-based multi-spectral X-ray image fusion method has been developed. This method utilizes Convolutional Neural Networks(CNNs) utilizes deep learning algorithms to combine multiple images obtained from different X-ray energy levels into a single image with higher resolution and improve quality. The problem statement highlights the limitations of traditional X-ray imaging methods and the challenges in developing an effective image fusion method. The proposed approach’s contribution is a step towards improving the quality and accuracy of medical imaging, leading to better patient outcomes and more efficient healthcare practices. The provided results indicate that the proposed model achieved an accuracy of 95% on the training data and 90% on the test data, with room for improvement. The limitation of the dataset and the algorithm used for classification are discussed as potential reasons for not achieving higher accuracy. Further research is required to develop specialized deep learning models for X-ray image fusion and explore other algorithms and techniques to address the challenges related to the dataset and the algorithm used for classification.
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