Detection of Brain Tumor using Convolution Neural Network through Detection of Metastasis

A novel model using convolution neural network has been developed for near accurate detection of brain tumor by carefully preprocessing the data and addressing class imbalance. Dataset was gathered between December 2017 and March 2018, consists of 7,001 JPG MRI pictures, 3,800 of which show a health...

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Bibliographic Details
Published inDevices for Integrated Circuit pp. 589 - 593
Main Authors Santra, Soumen, Sardar, Suman, Sil, Swarnadeep, Sarkar, Rudra, Deyasi, Arpan
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.04.2025
Subjects
Accuracy
Brain modeling
Brain tumor
Brain tumors
Convolution
Convolution neural network
Integrated circuit modeling
Neural networks
Pathology
Sensitivity
Testing
testing accuracy
Training
training accuracy
validation accuracy
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Summary:A novel model using convolution neural network has been developed for near accurate detection of brain tumor by carefully preprocessing the data and addressing class imbalance. Dataset was gathered between December 2017 and March 2018, consists of 7,001 JPG MRI pictures, 3,800 of which show a healthy brain, and 3,201 of which show a brain with tumors; results 94.14% accuracy in training, 94.33% accuracy in testing and 95.12% accuracy in validation. Insignificant losses are reported in all the cases, speaks in favor of quality of segregation between pathological and normal tissues. Present study provides 92.39% sensitivity, 96.63% specificity and 97.03% precision which clearly outperforms peer-reviewed works carried out using CNN; and therefore, justifies novelty of the work.
ISSN:2996-3044
DOI:10.1109/DevIC63749.2025.11012428