3 Phase Atrous Net with DCO-3DSPMRINET Model for Scoliosis Prediction - Adibatti, Spurthi; Sudhindra, K. R.; Manisha S., Joshi (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Intervertebral Disc (ID) is the mattress like structure that holds the bones of the spine together thus these discs increase the stability of the spinal column and ID images are used in the prediction of Scoliosis disease. However, while processing these images existing techniques use edge operators to locate four points on vertebral body for prediction of disc bulge but it is very difficult to obtain such image because the severity in the selected plane image is still uncertain. Hence a novel Quadruple Up Sampling Operation up sample the images double times with the Omnidirectional sagittal block matching algorithm that select, match and label the image hence, the intervertebral disc’s severity and the plane image as a slice of the present frame remain predictable. Moreover, during segmentation it is impossible for 3D structure to reconstruct automatically due to over segmentation and it provide only less detail about disease growing rate. Hence the technique 3 phase Atrous Net automatically segment and predict the sub image pair by segmentation and elastic atlas mapping with the cascade and parallel atrous convolution thus provide separation between vertebral disc near together and overcome over segmentation with the Duck colony optimization algorithm for feature selection. However, in classification stage sematic and discreet reconstruction limit the detail in image feature extraction so it results in perplexing outcome. Hence 3DSpMRINet technique uses sparse learning that provide feature weight vector that enhances the quality of the image and provide a high accuracy of disease classification. The proposed model for scoliosis prediction has been implemented in python platform and the result shows improved accuracy, recall rate, precision and F1 score.