Effect of Optimisation in Brain Tumour Classification with CNN-VIT Hybrid

  • Ivan Christopher Sukandar UPN ”Veteran” Jawa Timur
  • Fetty Tri Anggraeny UPN ”Veteran” Jawa Timur
  • Made Hanindia Prami Swari UPN ”Veteran” Jawa Timur
Abstract views: 82 , PDF downloads: 55
Keywords: Hibrida, Tumor Otak, Kecerdasan Buatan


A tumor is an abnormality in cells that causes cells that should live and die at a certain time to remain alive and grow abnormally. Tumors can affect all parts of the human body such as the brain. In diagnosing tumors, artificial intelligence can be used to perform classification or detection quickly. The use of artificial intelligence such as Convolutional Neural Network (CNN) has been commonly used to enter the medical field. Besides CNN, there are also other algorithms such as Vision Transformer (ViT) to do similar work. Therefore, a hybrid of the two methods is used to support the advantages of each algorithm and get satisfactory results. The results of the CNN-ViT hybrid used in this study obtained the highest results with an Adam Optimization with Learning Rate of 0.001% with an accuracy of 94%, recall and f1 score of 94% and precision of 95%.


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How to Cite
I. C. Sukandar, F. Tri Anggraeny, and M. Hanindia Prami Swari, “Effect of Optimisation in Brain Tumour Classification with CNN-VIT Hybrid”, antivirus, vol. 18, no. 1, pp. 112-124, Jun. 2024.