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In International Conference on ICT Innovations, pp. Journal of Neuroscience Methods, 362, 109296 (2021).ĭandıl, E., Çakıroğlu, M., & Ekşi, Z.: Computer-aided diagnosis of malign and benign brain tumors on MR images. S., Godefroy, O., & Aarabi, A.: Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images. Shahvaran, Z., Kazemi, K., Fouladivanda, M., Helfroush, M. Concurrency and Computation: Practice and Experience, e6541 (2021). Three-class brain tumor classification from magnetic resonance images using separable convolution based neural network. In 2021 7th International Conference on Advanced Computing and Communication Systems.

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M., & Apurva, C.: Design and analysis of 8-bit ripple Carry Adder using nine Transistor Full Adder.

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L., & Saikumar, K.: Implementation of online and offline product selection system using FCNN deep learning: Product analysis. In 2021 International Conference on Computational Intelligence and Knowledge Economy (pp. N., Saikumar, K., & Ratnayake, D.: Teaching and learning based 5G cognitive radio application for future application. Intelligent Systems Reference Library, vol 210. (eds) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems.

In: Kumar P., Obaid AJ, Cengiz K., Khanna A., Balas V.E. Machine Learning Based Online Handwritten Telugu Letters Recognition for Different Domains. Sankara Babu B., Nalajala S., Sarada K., Muniraju Naidu V., Yamsani N., Saikumar K. Raju K., Chinna Rao B., Saikumar K., Lakshman Pratap N.: An Optimal Hybrid Solution to Local and Global Facial Recognition Through Machine Learning. Saikumar, K., Rajesh, V.: A novel implementation heart diagnosis system based on random forest machine learning technique International Journal of Pharmaceutical Research 12, pp. Coronary blockage of artery for Heart diagnosis with DT Artificial Intelligence Algorithm. International Journal of Scientific and Technology Research, 9(3), 7076–7081(2020). R.: Logistic regression machine learning algorithm on MRI brain image for fast and accurate diagnosis. Saikumar.: A Robust And Accurate Video Watermarking System Based On SVD Hybridation For Performance Assessment, International Journal of Engineering Trends and Technology, 68(7),19–24 (2020). U.: Fast and accurate feature extraction-based segmentation framework for spinal cord injury severity classification. U., & Lay-Ekuakille, A.: A hybrid CONN-based segmentation and boosting classifier for real-time sensor spinal cord injury data. These experimental results are more accurate and outperform the methodology. An accuracy of 98.89% has been attained using the proposed deep learning technology. This model is a 13-layer framework compared with earlier models, this one performed efficiently.

The proposed deep-learning model is CONN The max-pooling layer is an advanced feature extraction layer, it is functioning on the input image and loads the features into a CSV file in terms of ImageNet format. At the last stage, going for deep-learning-based classification and decision, MRI spinal cord injury is identified with performance measures. At the first stage, pre-processing is applied, then segmentation is applied with the type of mean segmentation lesion technique. For testing purposes, six patients’ data are collected from Manipal imaging hospital, Gutur, India, and the training images are collected from the ANDI, MNIST, and GitHub database centers. In this work, MR images of spinal cord injury is estimated using deep-learning techniques. The rapid growth in radiology tends to provide solutions to MR images and their diagnosis.