3D CNN-Residual Neural Network Based Multimodal Medical Image Classification

3D CNN-Residual Neural Network Based Multimodal Medical Image Classification

© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : B. Suryakanth, S A. Hari Prasad
DOI : 10.14445/22315381/IJETT-V70I10P236

How to Cite?

B. Suryakanth, S A. Hari Prasad, "3D CNN-Residual Neural Network Based Multimodal Medical Image Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 371-380, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P236

Multimodal medical imaging has become incredibly common in biomedical imaging. From multimodality clinical visual information, meaningful information has been derived using clinical image classification. Computed tomography (CT) and Magnetic resonance imaging (MRI) are some imaging approaches. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. Compared to 1D and 2D images, 3D images have a very clear vision. The suggested approach uses 3D Residual Convolutional Neural Network (CNN ResNet) for the 3D image classification. Various methods are available for classifying the disease, like cluster, KNN, and ANN. Traditional techniques are not trained to classify 3D images, so an advanced approach is introduced in the proposed method to predict the 3D images. Initially, the multimodal 2D medical image data is taken. This 2D input image is turned into 3D image data because 3D images give more information than 2D image data. After employing guided filtering to integrate the 3D CT and MRI data, the resultant image is filtered for further processing. The fused image is then augmented. Finally, this fused image is fed to 3DCNN ResNet for classification purposes. The 3DCNN ResNet classifies the image data and produces the output as five different stages of the disease. The proposed method achieves 98% of accuracy. Thus, the designed model effectively predicted the disease's stage.

Designed modal, Fused image, Guided filtering, Multimodal medical image.

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